Methods › General › Prompt Engineering › CoOp
Context Optimization
CoOp
Introduced by Kaiyang Zhou et al. in Learning to Prompt for Vision-Language Models
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
CoOp, or Context Optimization, is an automated prompt engineering method that avoids manual prompt tuning by modeling context words with continuous vectors that are end-to-end learned from data. The context could be shared among all classes or designed to be class-specific. During training, we simply minimize the prediction error using the cross-entropy loss with respect to the learnable context vectors, while keeping the pre-trained parameters fixed. The gradients can be back-propagated all the way through the text encoder, distilling the rich knowledge encoded in the parameters for learning task-relevant context.
Papers archive 2025-07-28
30 shown of 30, 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.
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FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection 6 Jul 2025 · 1 repository · arXiv:2507.04511Syntology ran 3 of 9 samples · 6 unverified · 9 pointer-only (licence)
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MMRL++: Parameter-Efficient and Interaction-Aware Representation Learning for Vision-Language Models 15 May 2025 · 1 repository · arXiv:2505.10088
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MMRL: Multi-Modal Representation Learning for Vision-Language Models 11 Mar 2025 · 1 repository · arXiv:2503.08497Syntology ran 0 of 1 samples · 1 unverified
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Multi-Point Positional Insertion Tuning for Small Object Detection 24 Dec 2024 · 0 repositories · arXiv:2412.18090
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PLPP: Prompt Learning with Perplexity Is Self-Distillation for Vision-Language Models 18 Dec 2024 · 0 repositories · arXiv:2412.15277
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TextRefiner: Internal Visual Feature as Efficient Refiner for Vision-Language Models Prompt Tuning 11 Dec 2024 · 1 repository · arXiv:2412.08176
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TransAgent: Transfer Vision-Language Foundation Models with Heterogeneous Agent Collaboration 16 Oct 2024 · 1 repository · arXiv:2410.12183Syntology ran 6 of 8 samples · 2 unverified
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FLIER: Few-shot Language Image Models Embedded with Latent Representations 10 Oct 2024 · 0 repositories · arXiv:2410.07648
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Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models 3 Oct 2024 · 1 repository · arXiv:2410.02681Syntology ran 4 of 9 samples · 5 unverified
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Enhancing Robustness of Vision-Language Models through Orthogonality Learning and Self-Regularization 11 Jul 2024 · 0 repositories · arXiv:2407.08374
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Learning to Adapt Category Consistent Meta-Feature of CLIP for Few-Shot Classification 8 Jul 2024 · 0 repositories · arXiv:2407.05647
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IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning 19 Jun 2024 · 0 repositories · arXiv:2406.13683
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AAPL: Adding Attributes to Prompt Learning for Vision-Language Models 25 Apr 2024 · 1 repository · arXiv:2404.16804
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Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt Tuning 31 Mar 2024 · 1 repository · arXiv:2404.00603Syntology ran 4 of 6 samples · 2 unverified
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Concept-Guided Prompt Learning for Generalization in Vision-Language Models 15 Jan 2024 · 0 repositories · arXiv:2401.07457
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Text-driven Prompt Generation for Vision-Language Models in Federated Learning 9 Oct 2023 · 0 repositories · arXiv:2310.06123
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SwapPrompt: Test-Time Prompt Adaptation for Vision-Language Models 21 Sep 2023 · 0 repositories
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PRE: Vision-Language Prompt Learning with Reparameterization Encoder 14 Sep 2023 · 2 repositories · arXiv:2309.07760
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Language Models as Black-Box Optimizers for Vision-Language Models 12 Sep 2023 · 1 repository · arXiv:2309.05950Syntology ran 7 of 11 samples · 4 unverified · 11 pointer-only (licence)
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Context-Aware Prompt Tuning for Vision-Language Model with Dual-Alignment 8 Sep 2023 · 0 repositories · arXiv:2309.04158
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Unsupervised Prototype Adapter for Vision-Language Models 22 Aug 2023 · 0 repositories · arXiv:2308.11507
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MuDPT: Multi-modal Deep-symphysis Prompt Tuning for Large Pre-trained Vision-Language Models 20 Jun 2023 · 1 repository · arXiv:2306.11400
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LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning 2 Jun 2023 · 2 repositories · arXiv:2306.01293Syntology ran 5 of 12 samples · 7 unverified
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Task-Oriented Multi-Modal Mutual Leaning for Vision-Language Models 30 Mar 2023 · 0 repositories · arXiv:2303.17169
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Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models 4 Nov 2022 · 1 repository · arXiv:2211.02219
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Prompt Tuning with Soft Context Sharing for Vision-Language Models 29 Aug 2022 · 1 repository · arXiv:2208.13474
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Learning to Compose Soft Prompts for Compositional Zero-Shot Learning 7 Apr 2022 · 1 repository · arXiv:2204.03574Syntology ran 2 of 2 samples · 0 unverified
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Unsupervised Prompt Learning for Vision-Language Models 7 Apr 2022 · 1 repository · arXiv:2204.03649
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Conditional Prompt Learning for Vision-Language Models 10 Mar 2022 · 12 repositories · arXiv:2203.05557Syntology ran 4 of 6 samples · 2 unverified
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Learning to Prompt for Vision-Language Models 2 Sep 2021 · 18 repositories · arXiv:2109.01134Syntology ran 3 of 12 samples · 9 unverified
Tasks archive 2025-07-28
20 shown of 39 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections