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Methods › Computer Vision › Vision and Language Pre-Trained Models › CLIP

Contrastive Language-Image Pre-training

CLIP

3,094 papers tagged archive 2025-07-28

Introduced by Alec Radford et al. in Learning Transferable Visual Models From Natural Language Supervision

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Contrastive Language-Image Pre-training (CLIP), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning from natural language supervision. , CLIP jointly trains an image encoder and a text encoder to predict the correct pairings of a batch of (image, text) training examples. At test time the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes.

For pre-training, CLIP is trained to predict which of the N X N possible (image, text) pairings across a batch actually occurred. CLIP learns a multi-modal embedding space by jointly training an image encoder and text encoder to maximize the cosine similarity of the image and text embeddings of the N real pairs in the batch while minimizing the cosine similarity of the embeddings of the N² - N incorrect pairings. A symmetric cross entropy loss is optimized over these similarity scores.

Image credit: Learning Transferable Visual Models From Natural Language Supervision

PaperSourceSee Code · OpenAI/CLIP

Papers archive 2025-07-28

30 shown of 3,094, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 947 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Retrieval336
Language Modelling297
Zero-Shot Learning253
Image Classification250
Image Generation245
Contrastive Learning228
Semantic Segmentation219
image-classification218
Language Modeling193
Prompt Learning162
Segmentation161
zero-shot-classification158
Representation Learning143
Object142
Image Captioning117
Object Detection117
object-detection115
Text-to-Image Generation109
Transfer Learning107
Image Retrieval106

Usage over time archive 2025-07-28

Papers per year tagged with CLIP: 2021 to 2025, peak 1,191 1,191 0 2021: 101 papers 2021 2022: 359 papers 2022 2023: 887 papers 2023 2024: 1191 papers 2024 2025: 556 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (3,094 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Vision and Language Pre-Trained ModelsImage Representations

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