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How CLIP Aligns Images and Text for AI Generation

Added: 2026-08-07

Deep Dive

Basic concepts of vector embeddings and latent space representation for representing high-dimensional data.

1장. 임베딩, 잠재공간, 표현

1장. 임베딩, 잠재공간, 표현

704

Embedding is the process of mapping high-dimensional data (like text, audio, or video) into dense vectors where all elements contain real values, as opposed to sparse vectors with many zeros; this transformation helps neural networks extract meaningful patterns by avoiding zero values that would nullify weight multiplications. Latent space is a compressed vector representation where input characteristics are condensed into a smaller dimension, commonly used in autoencoders where data is compressed through a bottleneck layer and then reconstructed. Both embedding and latent space are forms of representation that convert raw data into numerical vectors, with embedding being more commonly used in natural language processing and language models, while latent space is frequently associated with autoencoders and generative models.

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Recommender Systems: Basics, Types, and Design Consideration | Machine Learning | Community Webinar

Recommender Systems: Basics, Types, and Design Consideration | Machine Learning | Community Webinar

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Embeddings are low-dimensional vector representations (typically 128-1024 dimensions) that capture the essential characteristics of items or users in a shared latent space. In content-based filtering, movies are embedded such that similar movies have vectors close together in this space. The dot product between two vectors indicates similarity - higher values mean more similar items. These latent dimensions represent meaningful features like genres, themes, or other characteristics, though they are learned automatically from data rather than defined manually.

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Qué son las BÚSQUEDAS SEMÁNTICAS y los EMBEDDINGS: IA con tu base de datos

Qué son las BÚSQUEDAS SEMÁNTICAS y los EMBEDDINGS: IA con tu base de datos

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Embeddings are mathematical representations of concepts as vectors in multi-dimensional space. The video demonstrates this using animals and colors as examples, showing how single dimensions can represent one attribute (animal type) while multiple dimensions represent multiple attributes simultaneously. Once concepts are vectorized, mathematical operations like subtraction and addition can create new conceptual representations. The key insight is that embeddings enable semantic search by positioning concepts in a space where proximity indicates similarity, allowing databases to find related items based on conceptual closeness rather than exact matches.

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Hoe creatief is kunstmatige kunst?

Hoe creatief is kunstmatige kunst?

635

Computers represent words as points in high-dimensional spaces (like 100,000 dimensions). Words that frequently appear together in text are placed close together in this space, creating a semantic map where words with similar meanings are spatially close. This allows computers to understand language relationships. In the latent space, each point represents a possible image. The computer can generate new images by creating new points in this space. When you take the average of two points (like two real faces), you get a new point representing an average face. This is how AI can generate realistic images of people who don't exist, like the famous 'this person does not exist' website.

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Building a RAG Application with Python, LangChain, and OpenAI

Building a RAG Application with Python, LangChain, and OpenAI

Embeddings map text concepts to points in multi-dimensional space, where semantically similar concepts appear close together and dissimilar concepts appear far apart. An embedding function takes text as input and returns a vector of numbers representing its position in this conceptual space. This mathematical representation enables efficient similarity computation between any two pieces of text, forming the foundation for vector search and retrieval-augmented generation systems.

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Deep Dive

Fundamentals of deep learning architectures, specifically Transformers for natural language processing and Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs) for computer vision.

[컴퓨터비전 2026] Lecture 11. Transformers II

[컴퓨터비전 2026] Lecture 11. Transformers II

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The Transformer is a sequence-to-sequence model with encoder and decoder layers. Keys calculate similarity with queries, while values provide representations for weighted combination. BERT (Bidirectional Encoder Representations from Transformers) uses only the encoder, combining word embeddings, positional encodings, and segment embeddings. Two pre-training tasks enable rich contextual understanding: Masked Language Modeling (MLM) randomly masks 15% of tokens and requires prediction based on context; Next Sentence Prediction (NSP) helps learn sentence-level relationships. Vision Transformer (ViT) applies Transformer architecture to computer vision by treating images as sequences of tokens. Images are divided into fixed-size patches (e.g., 16x16 pixels), flattened, and projected to vectors. ViT only outperforms CNNs on large datasets and models, requiring enormous resources (300 million images, 2,500 TPU cores, $480,000) because it lacks CNN's inductive biases of spatial locality and translation invariance.

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CNNs, RNNs, LSTMs, and Transformers

CNNs, RNNs, LSTMs, and Transformers

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This video explains the historical progression of deep learning architectures, starting with Convolutional Neural Networks (CNNs) for computer vision, which process images by extracting features from local patches and progressively compressing resolution; moving to Recurrent Neural Networks (RNNs) for sequential data like text, which pass hidden states through time but suffer from the vanishing gradient problem; then introducing Long Short-Term Memory (LSTM) networks that solve this issue through specialized gates (input and forget gates) to better retain information across sequences; culminating in the 2017 Transformer architecture, which eliminates sequential processing entirely by using self-attention mechanisms to weigh importance across all elements simultaneously, enabling parallel computation and superior performance. The video further demonstrates how Transformers are now being applied to computer vision through Vision Transformers (ViT), which treat images as sequences of patches, and how dual-transformer models like CLIP connect text and images by learning cross-modal representations.

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Best Computer Vision Roadmap ever: From Beginner to Pro

Best Computer Vision Roadmap ever: From Beginner to Pro

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This extensive section covers the evolution of deep learning architectures in computer vision. Phase 3 introduces Convolutional Neural Networks (CNNs) with learnable kernel filters that automatically extract features, convolution layers, pooling operations (max, mean, median), activation functions (ReLU, sigmoid, softmax), and image flattening for fully connected layers. Training involves loss functions, backpropagation, and regularization to prevent overfitting. Libraries like PyTorch and TensorFlow are used for implementation. Phase 4 covers transfer learning, leveraging pre-trained models (trained on large datasets like ImageNet) for new tasks through pre-training and fine-tuning, making it efficient for medical diagnosis and specialized applications. Phase 5 introduces Vision Transformer (ViT), which divides images into patches and processes them through transformer blocks with positional encoding, multi-head self-attention, and feed-forward networks, representing the modern approach following NLP transformer success. Practical assignments include dog vs cat classifiers, mask detection, digit classification, and various ViT-based projects. These phases require 5-8 weeks each of dedicated study.

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Vision Transformers explained

Vision Transformers explained

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Vision Transformers (ViT) are deep learning models that apply the Transformer architecture from natural language processing to computer vision tasks by breaking images into fixed-size patches (typically 16x16 pixels), flattening each patch into a token sequence, applying linear projection to reduce dimensionality, adding positional embeddings to preserve spatial information, and passing these through Transformer encoder layers with self-attention mechanisms to capture global dependencies, ultimately using a classification head for tasks like image classification without requiring convolutional neural networks.

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EfficientML.ai Lecture 2 - Basics of Neural Networks (MIT 6.5940, Fall 2023)

EfficientML.ai Lecture 2 - Basics of Neural Networks (MIT 6.5940, Fall 2023)

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Transformers use multi-head attention followed by feed-forward networks. Each attention head processes query, key, and value vectors through linear transformations, then computes scaled dot-product attention with softmax. Multiple heads operate in parallel. VGG uses homogeneous 3×3 convolutions throughout with homogeneous dimensions. Residual networks use bottleneck blocks: 1×1 (channel reduction), 3×3 (processing), 1×1 (restoration). MobileNet uses depthwise separable convolutions (grouped with G=channels) with inverted bottlenecks (expansion ratio 6). Each architecture balances parameter efficiency, computational cost, and accuracy differently.

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Deep Dive

The core philosophy of self-supervised learning, where models learn representation from unlabeled data.

L7 Self-Supervised Learning (Spring 2024, UC Berkeley) -- Pieter Abbeel & Philipp Wu

L7 Self-Supervised Learning (Spring 2024, UC Berkeley) -- Pieter Abbeel & Philipp Wu

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Self-supervised learning enables learning useful representations from unlabeled data by having the data provide its own supervision. The core idea is withholding part of the data and training to predict what was withheld. This approach addresses the challenge of obtaining labeled data, allowing models to leverage massive amounts of unlabeled data before fine-tuning for specific tasks. Good representations make downstream tasks easier by enabling networks to learn hierarchical features—from edges to shapes to semantic objects—making subsequent classification much more efficient.

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[CS6101] W10 « Unsupervised Distribution Alignment/The Revolution won't be supervised (Alexei Efros)

[CS6101] W10 « Unsupervised Distribution Alignment/The Revolution won't be supervised (Alexei Efros)

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Self-supervised learning proposes that labels are not inherently valuable but rather represent a bottleneck in machine learning. The core philosophy is that abundant unlabeled data contains sufficient information for learning if properly utilized. Instead of requiring human-labeled datasets, models can learn useful representations by solving pretext tasks derived from the data itself, such as predicting missing parts of images or reconstructing corrupted inputs.

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Webinar | Self Supervised learning : Approaches and Applications

Webinar | Self Supervised learning : Approaches and Applications

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Self-supervised learning is a representational learning paradigm that learns good data representations from unlabeled datasets by constructing supervised learning tasks internally. Unlike traditional supervised learning which requires expensive labeled data, SSL leverages the data itself to provide supervision. The core motivation stems from the high cost and scarcity of labeled data, making SSL valuable for transferring useful information to downstream tasks with few examples or requiring zero-shot transfer. Pretext tasks are central to SSL, where models predict missing parts of data from available information, such as predicting future from past, visible from invisible, or rotated versions from original images.

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SWaV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments (Mathilde Caron)

SWaV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments (Mathilde Caron)

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Self-supervised learning is a paradigm where models learn incredibly good and generalizable representations without needing semantic annotations. The core idea involves predicting the future from the past, predicting invisible from visible, predicting occluded or corrupted parts from available parts, or pretending there is an input which you know about. This approach allows learning from vast amounts of unlabeled data, which is particularly valuable since labeling images remains difficult despite easier data collection.

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Lecture 21 - Deep Learning Foundations by Soheil Feizi : Self-Supervised/Contrastive Learning

Lecture 21 - Deep Learning Foundations by Soheil Feizi : Self-Supervised/Contrastive Learning

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Self-supervised learning uses structure in unlabeled data to create artificial supervised learning problems. Without actual labels, we generate artificial labels from the data itself. We then train deep models to solve these artificial labeled problems, and the internal representations learned during this process become our embeddings. These representations are expected to be useful for unknown downstream tasks like classification.

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Deep Dive

An introductory understanding of contrastive loss functions, which aim to minimize distance between matching pairs and maximize distance between mismatching pairs.

Contrastive Loss : Data Science Basics

Contrastive Loss : Data Science Basics

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This section presents the complete mathematical derivation of contrastive loss. It begins with defining a binary variable Y (Y=1 for same-class, Y=0 for different-class) that acts as a mathematical switch. For same-class examples, square loss (L=D²) encourages embeddings to collapse toward each other. For different-class examples, a naive negative distance approach fails because it rewards infinite separation. The correct solution uses a margin-based formulation: L = max(0, M-D)², which only applies penalty when distance falls below the margin. The complete formula combines both cases: L = Y×D² + (1-Y)×max(0, M-D)². This elegant formulation enables models to learn meaningful embedding spaces where similar examples cluster while maintaining appropriate separation between different classes.

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Contrastive loss(es)

Contrastive loss(es)

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Contrastive loss is a training objective that learns meaningful representations by comparing pairs of samples—pulling together samples from the same class (positive pairs) and pushing apart samples from different classes (negative pairs)—with two main variants: one using Siamese networks with squared Euclidean distance and margin constraints, and another using augmented data with cosine similarity and exponential functions to maximize separation between positive and negative pairs.

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Deep Learning | S23| Lecture 9: Siamese Network, Variational Autoencoder, Generative Adversarial Net

Deep Learning | S23| Lecture 9: Siamese Network, Variational Autoencoder, Generative Adversarial Net

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The contrastive loss function is defined as: L = Σ[(1 - y_i) * d(f(x_i1), f(x_i2)) + y_i * max(m - d(f(x_i1), f(x_i2)), 0)], where y_i is 0 for similar pairs and 1 for dissimilar pairs, d is the distance metric, and m is the margin. When pairs are similar (y=0), only the first term applies, encouraging the distance to be minimized. When pairs are dissimilar (y=1), the second term applies, encouraging the distance to be maximized up to the margin m.

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Speaker diarization -- Herve Bredin -- JSALT 2023

Speaker diarization -- Herve Bredin -- JSALT 2023

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Contrastive loss minimizes distance between same-speaker embeddings while maximizing distance between different-speaker pairs with a margin. Triplet loss uses anchor-positive-negative triplets, minimizing distance between anchor and positive while maximizing distance to negative. However, triplet loss requires careful sample selection and often performs worse than modern alternatives.

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دوره contrative learning-جلسه دوم- بررسی max-marging loss and triplet loss

دوره contrative learning-جلسه دوم- بررسی max-marging loss and triplet loss

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Max Margin Loss (2005) and Triplet Loss (2015) are two fundamental loss functions in contrastive learning that aim to bring similar samples closer together in the embedding space while pushing dissimilar samples apart. Max Margin Loss uses pairs of samples (similar or dissimilar) and enforces a minimum margin (epsilon) between representations of samples from different semantic classes. Triplet Loss uses triplets (anchor, positive, negative) and simultaneously minimizes the distance between anchor and positive samples while maximizing the distance between anchor and negative samples, with a margin (alpha) ensuring the negative sample is at least alpha distance away from the anchor. Effective training strategies include using random triplets initially, then switching to hard positive/negative selection (choosing positives with maximum distance and negatives with minimum distance from the anchor) from mini-batches, while being cautious with noisy datasets.

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CLIP Basics

Caption Limits

Data Scale

Twin Encoders

Loss Matrix

Cosine Metric

Guiding Gen

Zero-Shot

Training Tie

CLIP Basics

0:00

Playing Section

1
Explains the need for aligning images with language in models.
2
Introduces CLIP as a solution for scalable visual-linguistic pairing.
3
Highlights limitations of traditional classifier approaches.

Deep Dive

The architecture of Latent Diffusion Models (e.g., Stable Diffusion) and how CLIP's text encoder guides the iterative denoising process.

Stable Diffusion - What, Why, How?

Stable Diffusion - What, Why, How?

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Diffusion models generate images through a two-phase process: first adding noise to an image step-by-step until it becomes pure noise (forward process), then training a neural network to reverse this by removing noise step-by-step (backward process). Stable Diffusion innovates by using an autoencoder to compress images into a latent space (reducing from 512x512 to 64x64), making computation feasible on consumer GPUs. This model gained popularity due to four factors: good results, free access, open-source availability with pre-trained weights, and low computational requirements. The architecture includes VAE for encoding/decoding, Tokenizer for text processing, CLIP Text Encoder for embeddings, UNet for denoising, and Scheduler for managing noise addition/removal across steps.

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Text to Image Diffusion AI Model from scratch - Explained one line of code at a time!

Text to Image Diffusion AI Model from scratch - Explained one line of code at a time!

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Conditional Latent Diffusion Models (CLDMs) generate images by iteratively removing noise from random noise through a U-Net architecture conditioned on text embeddings, where the process involves training an autoencoder to compress images into latent representations, then training a diffusion model on these compressed latents, and finally using CLIP-style text embeddings with attention mechanisms to condition the generation on textual prompts.

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【6分で分かる】Stable Diffusionの仕組み・特徴！

【6分で分かる】Stable Diffusionの仕組み・特徴！

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Stable Diffusion combines CLIP and Diffusion Models to generate images from text. CLIP (2021, OpenAI) converts images and text to vectors and calculates similarity scores to match appropriate text-image pairs. Diffusion Models (2020) add Gaussian noise to images and learn to reverse this process to reconstruct originals. Latent Diffusion Models (LDMs) improve efficiency by first converting high-dimensional images to lower-dimensional latent spaces before applying diffusion, reducing computational costs. Stable Diffusion uses LAION-5B (5.85B image-text pairs) filtered by CLIP, with LAION-400M selecting only aesthetically pleasing images for high-quality output.

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【お絵かきAI】Stable Diffusion の概要・使い方・ネットワーク構成を解説！😉【AIでテキストから画像生成！】

【お絵かきAI】Stable Diffusion の概要・使い方・ネットワーク構成を解説！😉【AIでテキストから画像生成！】

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This section explains the technical innovations behind Stable Diffusion's efficiency. The Latent Diffusion Model addresses diffusion model limitations by working with compressed latent representations rather than full images, significantly reducing computational costs while maintaining quality. Stable Diffusion's architecture consists of three components: a latent diffusion model for processing compressed representations, a frozen CLIP text encoder that converts user prompts into semantic vectors, and an autoencoder/decoder that converts latent variables into final 512x512 images. The text encoder guides the diffusion process by providing semantic guidance based on user input.

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George Hotz | Programming | stable diffusion, in tinygrad?!? can it happen? | Part1

George Hotz | Programming | stable diffusion, in tinygrad?!? can it happen? | Part1

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Stable Diffusion consists of three main components: (1) a diffusion model resembling a Transformer with input, middle, and output blocks, (2) a first-stage autoencoder that compresses images from 3 channels to 4 channels in latent space, reducing spatial dimensions from 512x512 to 64x64, and (3) a text encoder (CLIP model) that converts text prompts into embeddings. Diffusion models are probabilistic models designed to learn data distributions by gradually denoising normally distributed variables, relying on a re-weighted variant of the variational lower bound. The autoencoder enables dimensionality reduction before the diffusion process, making training and inference more computationally efficient. The vector quantizer outputs a diagonal Gaussian distribution, modeling uncertainty in the latent space representation. Implementing complex neural networks involves numerous challenges including matching exact layer configurations between frameworks, handling normalization layers with correct channel dimensions, implementing attention mechanisms with proper query, key, and value transformations, and managing tensor shapes through encoder-decoder architectures.

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Deep Dive

Zero-shot transfer learning and how CLIP can perform image classification on unseen datasets without task-specific training.

JetBrains AI Club, Lecture 16. CLIP. Fine-tuning and lin-prob

JetBrains AI Club, Lecture 16. CLIP. Fine-tuning and lin-prob

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CLIP enables zero-shot classification without any task-specific training: (1) Encode the input image using the image encoder; (2) Encode class names (or templates) using the text encoder; (3) Compute similarity scores between the image encoding and each class encoding; (4) The class with the highest similarity score is predicted as the classification result. This works because CLIP was pre-trained on millions of image-caption pairs, learning general semantic representations that can be transferred to new classification tasks. The model can classify images into classes it has never seen during training.

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Lec 10: ViT and Foundation Models | CSE351 Computer Vision | Fall 2025

Lec 10: ViT and Foundation Models | CSE351 Computer Vision | Fall 2025

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CLIP achieves zero-shot transfer by learning cross-modal relationships during pre-training. When presented with a new image, the model generates an image embedding. When given a text query, it generates a text embedding. The model then computes similarity scores between these embeddings to determine if the text describes the image. This allows CLIP to perform tasks like image classification or object detection without being explicitly trained on those specific tasks.

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Contrastive Language-Image Pre-training (CLIP)

Contrastive Language-Image Pre-training (CLIP)

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This comprehensive section covers CLIP's complete technical foundation and performance evaluation. Traditional vision systems use fixed categories requiring retraining for new concepts, while natural language supervision proves more data-efficient. The WIT dataset of ~400 million image-text pairs was constructed using 500,000 queries from Wikipedia. CLIP uses contrastive pre-training where the model learns to identify valid image-text pairings within mini-batches, achieving 4x efficiency improvement over bag-of-words baselines. Image encoders include ResNet-50 with attention pooling and Vision Transformers with layer normalization. Text encoders use byte-pair-embedded text with vocabulary size 49,000. Training involved five ResNet variants and three Vision Transformer variants optimized for 32 epochs on large-scale GPU clusters. Zero-shot transfer evaluates generalization to unseen datasets using templates like 'a photo of a [label]' with cosine similarity comparison. Prompt engineering significantly improves performance—templates instead of raw labels gave 1.3% gain on ImageNet, while ensembling over 80 contexts improved performance by 3.5%. Evaluation across 27 diverse datasets showed mixed results: CLIP excelled on Stanford Cars and Food-101 but underperformed on Flowers-102 and FTVC Aircraft. On video datasets (Kinetic-700, UCF-101), CLIP strongly outperformed, likely because natural language provides verb supervision. Zero-shot CLIP achieves performance equivalent to 16-shot supervised baselines.

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Contrastive Language-Image Pretraining (CLIP)

Contrastive Language-Image Pretraining (CLIP)

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CLIP trains using symmetric cross entropy loss, computing both image-to-text and text-to-image losses then averaging them. This maximizes cosine similarity between matching pairs while minimizing it for non-matching pairs. Zero-shot classification enables classifying images without task-specific training: encode the image and candidate text prompts, compute cosine similarities, apply softmax. More descriptive prompts yield higher probabilities—for instance, 'two cats lying on a sofa next to two TV controllers' outperforms generic prompts. The temperature parameter controls probability distribution sharpness.

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OpenAI's CLIP for Zero Shot Image Classification

OpenAI's CLIP for Zero Shot Image Classification

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CLIP (Contrastive Language-Image Pre-training) is a multimodal model that achieves zero-shot image classification by learning to encode both images and text into a shared 512-dimensional vector space during pre-training on 400 million image-text pairs, allowing it to classify images into new categories without additional training simply by comparing image embeddings with text-based class labels using cosine similarity.

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Deep Dive

Advanced multimodal AI systems, such as vision-language models (e.g., LLaVA, GPT-4V) that perform complex reasoning across image and text modalities.

Teaching AI to See: A Technical Deep-Dive on Vision Language Models with Will Hardman of Veratai

Teaching AI to See: A Technical Deep-Dive on Vision Language Models with Will Hardman of Veratai

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DeepMind's Flamingo model uses a cross-attention architecture where a frozen language model (Chinchilla) is connected to a vision encoder through cross-attention layers. The key innovation is the Perceiver Resampler, which reduces variable-length visual token sequences to a fixed dimensionality (64 tokens) before cross-attention, solving the challenge of handling images of different sizes and resolutions. The model uses interleaved image-text data from web documents, which was found to be crucial for maintaining downstream performance. LLaVA (Large Language and Vision Assistant) uses an auto-regressive architecture where vision tokens are projected into the language model's embedding space using a simple linear layer, then injected directly into the decoder stream. The key innovation was instruction tuning using synthetic data generated by GPT-4 from COCO captions with bounding boxes, creating 150,000 instruction-following examples that enabled LLaVA to outperform previous VLMs on complex reasoning tasks. MMMU (Massive Multidiscipline Multimodal Understanding) is a benchmark testing advanced multimodal reasoning across 30+ academic subjects with approximately 11,000 multiple-choice questions. When released in November 2023, GPT-4V scored 55% (random guessing is 25%), while LLaVA scored 34%. By recording date, GPT-4o had jumped to 78%.

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LLaVA paper - Comprehensive dissection

LLaVA paper - Comprehensive dissection

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This section presents the state-of-the-art results achieved by the Lava model and demonstrates advanced ensemble strategies. On the Science QA benchmark, Lava achieves 92.53% accuracy when combined with GPT-4 through careful orchestration. Two ensemble strategies are evaluated: GPT4-complement (GPT-4 helps when Lava fails) achieves 90.97% accuracy; GPT4-judge (GPT-4 evaluates between Lava's answer and its own) achieves 92.53% accuracy. Humans score 88%, while text-only GPT-4 scores 82.69%. The results demonstrate that combining visual reasoning capabilities with text-only reasoning through complementary orchestration yields superior performance. This represents a significant advancement in multimodal reasoning, showing that the synergy between visual and textual processing can outperform either modality alone when properly integrated.

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OpenClaw, realtime AI video games, agent swarms, AI Earth, new AI video models: AI NEWS

OpenClaw, realtime AI video games, agent swarms, AI Earth, new AI video models: AI NEWS

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State-of-the-art AI systems are achieving unprecedented capabilities in reasoning, coding, and cross-modal understanding. Kimi K 2.5 demonstrates agent swarm functionality enabling parallel execution of up to 100 agents simultaneously, while Qwen 3 Max Thinking incorporates adaptive tool use and test-time scaling to achieve competitive performance against closed models on scientific reasoning, mathematical problem-solving, and coding benchmarks. These systems can conduct deep research, generate comprehensive reports with cited sources, and synthesize information from multiple domains. The integration of reasoning capabilities with creative generation represents a fundamental convergence in AI development, enabling systems that can not only produce content but also understand, analyze, and reason about it in sophisticated ways.

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New LLaVA AI explained: GPT-4 VISION's Little Brother

New LLaVA AI explained: GPT-4 VISION's Little Brother

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Multimodal AI systems combine pre-trained vision transformers (like CLIP) with large language models (like Llama/Vicuna) using a projection layer to map visual features into language embedding spaces, enabling the model to understand and describe images through instruction-following datasets generated by large language models like GPT-4; LLaVA 1.5 improves upon earlier versions by using a multi-layer perceptron (MLP) instead of a simple linear projection and incorporating scientifically-oriented datasets, achieving state-of-the-art performance across multiple benchmarks while remaining computationally efficient enough to train within 24 hours on standard GPU infrastructure.

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China Just Beat the Entire AI Industry With One Insane Release: Beats OpenAI in 38 Tests

China Just Beat the Entire AI Industry With One Insane Release: Beats OpenAI in 38 Tests

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Seed 1.5VL combines a 532 million parameter vision encoder with a 20 billion parameter mixture-of-experts LLM to achieve multimodal reasoning capabilities. Trained on over three trillion high-quality tokens across text, images, video, and audio domains, the model tops 38 out of 60 public VLM benchmarks. Key innovations include dynamic frame resolution sampling for video processing, which adapts frame rates and resolution based on content complexity, enabling strong spatial-temporal reasoning within fixed token budgets.

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Deep Dive

Practical applications in semantic image retrieval, cross-modal search engines, and vector database querying using aligned text-image embeddings.

Semantic Search: A Deep Dive Into Vector Databases (with Zain Hasan)

Semantic Search: A Deep Dive Into Vector Databases (with Zain Hasan)

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Vector databases extend beyond text to handle images, audio, video, and other modalities since they operate on numerical vectors regardless of input type. Contrastive learning unifies different modalities by training models to bring together representations of the same concept across data types while pushing apart unrelated concepts. This creates unified embedding spaces where cross-modal search becomes possible—queries in one modality can retrieve relevant content in another. Two approaches exist: unified models that learn all modalities simultaneously versus modular approaches using separate specialist models per modality. Modular approaches offer practical implementations today, while unified models remain challenging to train.

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Embeddings: What they are and why they matter

Embeddings: What they are and why they matter

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CLIP demonstrates how embeddings can bridge different data modalities by placing text and image representations in shared vector spaces. The speaker shows browser-based demos comparing text queries against images, enabling applications like finding similar photographs or identifying visual themes. A practical example involves building a bathroom fixture finder that recommends products visually similar to expensive examples, illustrating how multimodal embeddings transform niche applications into viable business solutions.

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DEF CON 33 - Exploiting Shadow Data from AI Models and Embeddings - Patrick Walsh

DEF CON 33 - Exploiting Shadow Data from AI Models and Embeddings - Patrick Walsh

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Vector embeddings are mathematical representations created by embedding models that convert text into fixed-size arrays of numbers (typically 300-3,000 dimensions). Documents are chunked into sentences or paragraphs, and each chunk generates an embedding vector. Similar concepts appear close together in vector space (e.g., 'I'm happy today' and 'I feel great' are near each other), while dissimilar concepts appear far apart. Vector databases use these embeddings to perform semantic searches - when a user asks 'What's the irrigation status?', the system finds the closest matching vectors (like 'I need to fix the sprinklers') and retrieves relevant context. This technology powers facial recognition, image tagging, multimodal search, and document classification across virtually all modern software systems.

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生成AIを活かすには埋め込みの理解から！RAGの心臓：埋め込みの基礎から応用まで！

生成AIを活かすには埋め込みの理解から！RAGの心臓：埋め込みの基礎から応用まで！

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This section demonstrates practical embedding applications: text similarity search uses cosine similarity between query and document vectors to find relevant content, enabling cross-lingual search (English queries finding Japanese documents). Multi-modal embeddings combine text and image data into unified vector spaces, enabling cross-modal search (text-to-image, image-to-text, mixed content search). Applications include visual search on e-commerce sites where users find products by uploading photos or describing them. The process involves converting images to base64-encoded data URLs, generating embeddings for both modalities, and calculating cosine similarity. Classification accuracy depends heavily on label wording, and complex tasks may require additional classifiers.

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Image Search Engine in Python - Multimodal Embeddings

Image Search Engine in Python - Multimodal Embeddings

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Semantic image search leverages multimodal embedding models that place images and text in the same high-dimensional vector space, positioning similar concepts close together. This enables text-based queries to find matching images without manual labeling. The technical foundation includes installing dependencies (Transformers, Sentence Transformers, PyTorch, Qdrant Client, Pillow), loading pretrained models like Jina-CLIP V2, and configuring hardware (GPU/CPU). The vector database architecture stores embeddings with unique identifiers, vectors, and payload metadata, enabling efficient nearest-neighbor searches based on cosine similarity.

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CLIP Basics

Caption Limits

Data Scale

Twin Encoders

Loss Matrix

Cosine Metric

Guiding Gen

Zero-Shot

Training Tie

CLIP Basics

0:00

Playing Section

1
Explains the need for aligning images with language in models.
2
Introduces CLIP as a solution for scalable visual-linguistic pairing.
3
Highlights limitations of traditional classifier approaches.

330.5K views9.2K18:04@ComputerphileOriginal Release: 2024-04-25

CLIP (Contrastive Language-Image Pre-training) is a machine learning technique that creates a unified numerical embedding space where both images and text are represented as vectors, enabling AI systems to understand and generate images based on text descriptions by training on massive datasets of image-caption pairs (originally 400 million, now expanding to billions) using contrastive learning to maximize similarity between matching image-text pairs while minimizing similarity between mismatched pairs, with cosine similarity serving as the primary metric for measuring vector alignment in this shared high-dimensional space.

[00:00:00]so I thought we could talk a bit more about you know large language models AI image stuff in a previous video we looked at stable diffusion and image generation using diffusion and at one point in the video I basically handwaved off this kind of text embedding text prompt thing we embed this by using our GPT style Transformer embedding and we' stick that in as well right so the idea is that you've got this model that you've trained to try and produce new images but you want to be able to write some text that explains what you actually want in the image and how do we give it that text how do we say I want a frog on stilts right how you know how do we pass the the text frog on stilts into a network what we're trying to do is we're trying to represent an image in the same way that we represent language within a model that's the idea and it's and we call this process clip right or contrastive language image pairs so there are lots of times in AI where what we want to do is take an image and repres present it in some way that we can talk about it right so when you're talking to you know one of the more recent chat gpts or um co-pilot on you know on Windows or whatever you can put an image in and you can say what's in this image and it will try and explain it to you or you can say what happens in this image when something you know and it can it can kind of Reason about these things or at least that's the the implication of what the text it's producing is right that's that's for a different day the question I supp suppose is how do we turn an image into that kind of language so that we can then talk about it so one way of doing this is that you could just train up a classifier so you could say I've got my image net classification task with a thousand classes cats dogs airplanes buses hotels whatever and you just train your thousand class classifier on all images and then if you want text from the image all you do is you put it into the classifier it says it's a cat so you just you've got the word cat right you've solved it now this isn't a particularly effective solution because there are probably more than a thousand things on earth right at least I think there are I haven't listed them so if you train your thousand class or even 10,000 class classification problem it's only still going to work on those things you trained it on and even then it might get it wrong so it doesn't scale particularly well okay and so every time you want to introduce A New Concept so let's say you want a specific type of cat or you want a new species of animal that you haven't considered before you've got to go back to the drawing board collect a whole new bunch of data and then train again so that doesn't really really work at all so the next thing you could do is you could say okay well let's try and cut out this this classifier and just we'll just predict text right so this is quite a common thing to do so for a long time captioning was was essentially the goal of this kind of process so what you would do You' have an image and you have a network or a Transformer that's trained to just spit out a sentence that describes that image and this is quite a popular uh problem that's been solved in computer vision literature so you put an image and you say this is a man sitting in front of a boat or something like this this works better in the sense that you can get better text out right instead of just boat or man you can get this is a man in front of a boat but it has that same problem of scale if man is standing in front of a hotel and you haven't given it any examples of that maybe it won't work quite so well so what we haven't really managed to do is any kind of scalable way of pairing images and their meaning to the text that describes them right and that's what a clip embedding does right the idea is that we're going to try and find some kind of embedded numerical space where the images and the text are now in the same place this this a bit like the word vectoring that we it's quite a lot like that except that we've trained the vectors to align both between text and images right so the idea is imagine some high dimensional numerical space where everything has a fingerprint but that if you take an image and the text represents that image they'd have the same fingerprint and that way you can go ah they're the same thing you know or at least that that caption represents this image so how would we do that well the first thing you need to do is collect an absolutely massive amount of data right so the sort of size I mean this the clip paper that came out in fact I've got I've got a copy of it so this paper is from 2021 they collected a data set of 400 million image caption pairs which we can talk about that collection process in a moment that will be considered quite small by today's standards right today 5 billion might be a more reasonable number of images now downloading 5 billion images is not very easy on your laptop right if you down if you get a cluster of servers going it still might take you a week right this is a monstrously large amount of data but there is a lot of stuff on the internet not all of it good I should add anyway so the policy was basically go on the Internet and try and find images that have captions right so either in alt text or sitting nearby on the website or you know something like this and then try and find captions that have some usable interesting information so you don't want like an is ISBN number for a book right that's not very helpful captioning um you want a description of what's in the image some of the descriptions are going to be very good like a man in front of a boat wearing a red jumper and all this stuff some of them are going to be a dog and you know and you sort of you take what you can get right when you're scraping the internet some of them are going to be known classes some of them are going to be should we say not safe for work classes right some of them very problematic and so but you unfortunately there's now so many images you can't really look through very well and find them so is this mainly still images or all still it's all still images right you could do this on videos but but that's a separate piece of work let's say right so so you go on your web you you you get a web crawler going which is a bit like you know one of the search engine might use but it's specifically designed to go and try and find images that have captions with useful words in which you can then download and now you've got your 400 million images and what you want to do is train a model of some description that Maps those text Pairs and those image pairs together so let's start with a classifier right could we do that we could have a 4 million classification problem doesn't make any sense but that's not going to work um also some of the prompts are going to be similar but not quite the same a red cat a black cat isn't red cats but anyway know what you mean but you can't you can't control what you're going to find on the internet right so absolutely but the point is those are very similar they're sort of the same class right but they're not so you know a class-based system where you're going to try and put things in specific categories isn't really going to work what we want to do is find a way of embedding this these play into a sort of numerical score right represents so here's how we're going to do for our four million image data set right or even bigger we've got a bunch of image and text pairs so I'm going to represent text as just a line right it's it's actually a numerical encoding of that text because of course you can't actually put a string of text into a new network you turn it into numbers first right so we have an image and we have an image and we have an image and we draw them this way all the way up to image 400 million or something like this so I'm I'm going to leave some out of my drawing right quite lazy actually personally I've got there's some more paper here this is why you do this on a computer and not by hand so what we're going to do is we're going to create two networks we're going to have the images go into what we call a vision Transformer a vision Transformer and that is a Transformer model like we've talked about before sort of same sort of Transformer models you see in things like defusion or big large networks and it's going to put this into this embedded space so the input is an image with let's say red green and blue channels and the output is a numerical Vector that says there's this in the image right and we don't know what that really means on the other side we're going to have our text string and we're going to put this through a normal text Transformer similar to the way that you would structure something like chat GPT right so this is just going to be text Transformer I don't know how I don't know what the code for that is I'm just going to write T and what we're going to try and do is across this entire data set train it so that these embed to the same place when they're rep pair and when they're not a pair they embed to different places right because there different things in the image so how do we train this well what we do is we take a batch of images let's say five images so let's say our batch or our mini batch is five okay so we're going to have five images with five bits of text that go with those images and we're going to put them through our vision Transformer and through our text encoder right so we're going to get image image image image image nearly page we made it text text text text text and I thought briefly about drawing in different images in here and then I realized that that would look really bad because my cat looks a lot like my dog and my horse so I'm going to pretend we've got very nice pictures in there now if we embed this we're going to get an embedding for this one and we're going to get an embedding for this one it's going to be some feature Vector we don't know what it means and what we're going to do is we're going to calculate the distance between the embedding of this and the embedding of this let's just call that a distance D and we're going to going to have a distance between these ones we're going to have another distance here another distance here another distance here we're going to have a distance between this one and this one that's going to be a different distance my notation is all off so let's call this D1 D2 or whatever right it doesn't really matter it's a big Matrix of of distances right D3 D4 D5 now what we've done is we've set this up in a way where this is the first pair and this is the second pair and this is the third pair so the good stuff that we want the embeddings that we want to be the same are on the diagonal right all all the other embeddings we're going to say that we want to dissuade you from from coming up with a similar embedding in a similar image so what we do when we train it is we take our entire batch we calculate all of the embeddings for all of the different uh images and text and then we calculate the distance between each embedding and we're going to maximize the distances on the diagonal and for the sake of nice colors minimize all the other distances in The Matrix and we're going to do that over and over again again for 400 million images we're going to train this viit and we're going to train this T to encode these things so that they meet in the middle like this or if they're a different image so if it's a picture of a cat but the text says a man in front of a boat it doesn't embed them into the same place it embeds them into very different places now the metric we use for this is something called cosine similarity you could use different metrics cosine similarity is best easiest to think of it as the angle between two numbers right so let's imagine that you have a two-dimensional uh just show a quick one let's imagine your feature embedding is only two right so you have two things this is a bit like when we did face ID right when we're talking about phones oh when you can unlock your face with a phone that's the one if you want to unlock a face with your phone right you have an embedding over here you have an embedding over here and this is the angle between them if you have a different embedding over here that's a bigger angle and so these are more similar than these two and cosine similarity just measures this angle but in a higher dimensional space it's like an angle of vectors so it's an angle of a vector in like a 256 or element space so this works really really well we actually we're not training the images we're not training this Matrix we're using this Matrix to train this encoder and this encoder here right so they're taking images putting it in the medage space taking text putting it in the better space okay and so the end of the question is well okay we've done that right let's say we've done over 400 million images now what do we do with this well the point now is that you've got a way of representing the meaning or the content of a photo in the same way as you can represent captions of that photo or captions of a different string so now let's imagine you were trained let's just show a couple of examples of a sort of things you could do right clip gets used in a lot of places right for a lot of what we call Downstream tasks so the downstream tasks are how you use a clip once it's trained so let's imagine you've got your diffusion model which is trying to produce a lovely image of something that you you've written in your prompt so you have your gaussian noise and you have your denoise noising process which is trying to produce a nice new lovely image of a of a person mouth ears right here there we go I'll stop now now but we want to put some text in to say we want a picture of a person instead of a picture of a frog or something like this so in in here what we do is we take a man a man in front of a boat which I'm now going to have to draw in so know there we go right and we put that in we embed it using the text encoding that we've just created using our clip training so this turns this into a big set of numbers let's say n.1 minus 0.5 all the way along right it's very large list of numbers but doesn't literally say a man in front of a boat it just embeds the kind of meaning of that sentence in the same way you could embed an image of a man in front of a boat and then what you do is you insert this this into the network during the training process to say that's the guidance I want to give you when I skipped over this in the stable diffusion video that's what it's doing it's taking a pre-trained clip encoder encoding the text to guide the generation of the image let's think of a different Pro problem so one of the nice things about clip or one of the things that the advocates for clip would say was a really positive application of it is what we call zero shot classification for example so zero shot means you're classifying images despite never ever having been asked or trained to classify an imag so how would you do that well it's a bit weird right because if you think about you've got something that will embed an image into our embedded space and you've got something that will embed text into unembedded space but you can't undo that process it doesn't work in Reverse so that means we can't take a text string embed it and then uned it into the image you can only go this direction right this this direction here and this direction here so let's imagine I wanted to find out what was in an image but I didn't want to bother using this classifier training program that I'd come up with I want to use clip to do this right they've been they've trained on 400 million images got to se a cat before I should be able to classify cats right if I want to so what do we do so it's slightly odd what we do is we first embed a bunch of strings that say the image contains a cat right so we say we have a string that says a photo of a cat now of course it might not be a cat right so we also have a photo of a dog and we keep this going for all the different classes that we have all the way down to a photo of a bus right those are the three things I can think of but you actually have to write this physically write the sentence out and then embed it using this into your embedded space you embed this you embed this you embed this and these are essentially your lookups right these are the embedded representation of that sentence and what we're doing is we're trying to find which of these is closest to the embedded representation of His Image so we take the image that we're trying to classify which has got a picture of a cat in it how how do you draw a cat sort of rabbity Catty thing right you embed this into this into this cat embedding right this is our sort of this is our kind of test embedding and now we have to see which of these are closest to it we measure that coine distance and we can say well okay actually it's closest to the the sentence a photo of a cat so what we haven't done is explicitly say this pictures of a cat we've said that if you embed this picture using our clip embeddings and you also embed the phrase a photo of a cat those things are quite similar which implies that it is a picture of a cat right now that is not fullprof in any sense and also if you're thinking that all seems very inefficient right couldn't you just train a classifier yes right for some problems I think training a classifier is by far the easiest way of doing this but this gives you this kind of scalable way of doing it where you don't actually have to explicitly tell it what to do you can hope it comes out in the wash later before when you said inject this into the stable diffusion does the diffusion have to have seen the embeddings how does it know how are you talking in the same space okay yeah well it gets learned during the process so the idea would be that suppose you're doing the training rather than the the inference right you're not producing new images you're training you take an image of a cat right again I'm going to have to right and it needs a whiskers I'm sorry but that's the cat now it's not a good one though is it now you have a you know picture of a cat right and and this is your training pair so you no longer have just an image that you're trying to construct so what you do is you add some amount of noise to it and you train your network to say this was the noise I added or this was the reconstructed image right so this is a clean image of a cat which is now different but close enough but you also give it this text so what you're saying is you're you're learning that given an image that looks like a noisy cat and the text that it's meant to be a picture of a cat give me a clean image of a cat right and over time it learns to do this in a General case and it learns to do it where you can put different text in here that's the idea but you have to do this during training because if you didn't ever give it this text during training there'd be no way to tie the two concepts together so this network learns this over time um a lot of this it requires massive massive scale if you want to get really good images with really nice nuanced text prompts you have to have a lot of examples because if you just have a few dozen cats it's going to be very poor and it's not going to properly reflect what you you could say a picture of a cat but you couldn't say a picture of a cat wearing this and in in this place and and it is not very powerful so things like stable diffusion and darly have been trained over massive image and text sets specifically so that they have this generalizable property or at least they are somewhat more generalizable than they would otherwise be eventually the network is going to start to learn how to I mean actually that's not right cuz Dave's far away from Dave right so hopefully we start to we come together if we just sort of feel our way towards it by taking off little little bits of noise at a time we can actually produce an image right so you start off with a noisy image

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