# PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts

arXiv: https://arxiv.org/abs/2308.01313 · HTML: https://mlsanity.com/paper/2308.01313/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Prompt learning and adaptation of CLIP, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 41 of its 56 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Placed in

- Multimodal LLMs / vision-language models / [Prompt learning and adaptation of CLIP](https://mlsanity.com/mllm/prompt-learning-and-adaptation-of-clip/)

## Builds on (most cited first, 30 of 41)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [Learning Robust Global Representations by Penalizing Local Predictive Power](https://mlsanity.com/paper/1905.13549/) (2019)
- [When and why vision-language models behave like bags-of-words, and what to do about it?](https://mlsanity.com/paper/2210.01936/) (2022)
- [MedCLIP: Contrastive Learning from Unpaired Medical Images and Text](https://mlsanity.com/paper/2210.10163/) (2022)
- [Just Train Twice: Improving Group Robustness without Training Group Information](https://mlsanity.com/paper/2107.09044/) (2021)
- [Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models](https://mlsanity.com/paper/2209.07511/) (2022)
- [Prompt-aligned Gradient for Prompt Tuning](https://mlsanity.com/paper/2205.14865/) (2022)
- [Visual Classification via Description from Large Language Models](https://mlsanity.com/paper/2210.07183/) (2022)
- [What does a platypus look like? Generating customized prompts for zero-shot image classification](https://mlsanity.com/paper/2209.03320/) (2022)
- [Improving CLIP Training with Language Rewrites](https://mlsanity.com/paper/2305.20088/) (2023)
- [Debiasing Vision-Language Models via Biased Prompts](https://mlsanity.com/paper/2302.00070/) (2023)
- [Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications](https://mlsanity.com/paper/2108.02818/) (2021)
- [Unsupervised Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2204.03649/) (2022)

## Built on by (most cited first, 17 of 17)

- [Pixels Versus Priors: Controlling Knowledge Priors in Vision-Language Models through Visual Counterfacts](https://mlsanity.com/paper/2505.17127/) (2025)
- [DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language Models](https://mlsanity.com/paper/2408.08855/) (2024)
- [A sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification](https://mlsanity.com/paper/2409.13676/) (2024)
- [Order Matters: On Parameter-Efficient Image-to-Video Probing for Recognizing Nearly Symmetric Actions](https://mlsanity.com/paper/2503.24298/) (2025)
- [ContextHOI: Spatial Context Learning for Human-Object Interaction Detection](https://mlsanity.com/paper/2412.09050/) (2024)
- [Tile-Based ViT Inference with Visual-Cluster Priors for Zero-Shot Multi-Species Plant Identification](https://mlsanity.com/paper/2507.06093/) (2025)
- [Representation-Level Counterfactual Calibration for Debiased Zero-Shot Recognition](https://mlsanity.com/paper/2510.26466/) (2025)
- [CoAPT: Context Attribute words for Prompt Tuning](https://mlsanity.com/paper/2407.13808/) (2024)
- [Fairness-Aware Test-Time Prompt Tuning](https://mlsanity.com/paper/2608.25707/) (2026)
- [Noise-Tolerant Few-Shot Unsupervised Adapter for Vision-Language Models](https://mlsanity.com/paper/2309.14928/) (2023)
- [DoubleCCA: Improving Foundation Model Group Robustness with Random Sentence Embeddings](https://mlsanity.com/paper/2411.16236/) (2024)
- [Density-Aware Translation of Spurious Correlations in Zero-Shot VLMs](https://mlsanity.com/paper/2606.01710/) (2026)
- [SADL: What to Ignore? A Benchmark for Subject-Aware Distractor Localization](https://mlsanity.com/paper/2606.30393/) (2026)
- [ProCal: Inference-Time Proposal Calibration for Open-Vocabulary Object Detection](https://mlsanity.com/paper/2607.01759/) (2026)
- [Re-calibrated Contrastive Loss for Transformation-Aware Prompt Conditioning in Vision-Language Models](https://mlsanity.com/paper/2609.06967/) (2026)
- [Combining Hierarchical Cognitive Process with Process Supervision for Interpretable Scene Safety Understanding](https://mlsanity.com/paper/2609.26399/) (2026)
- [From Transformation to Target State: Rethinking Query Representation for Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2610.05993/) (2026)

## Abstract (arXiv)

Vision-language models like CLIP are widely used in zero-shot image classification due to their ability to understand various visual concepts and natural language descriptions. However, how to fully leverage CLIP's unprecedented human-like understanding capabilities to achieve better performance is still an open question. This paper draws inspiration from the human visual perception process: when classifying an object, humans first infer contextual attributes (e.g., background and orientation) which help separate the foreground object from the background, and then classify the object based on this information. Inspired by it, we observe that providing CLIP with contextual attributes improves zero-shot image classification and mitigates reliance on spurious features. We also observe that CLIP itself can reasonably infer the attributes from an image. With these observations, we propose a training-free, two-step zero-shot classification method PerceptionCLIP. Given an image, it first infers contextual attributes (e.g., background) and then performs object classification conditioning on them. Our experiments show that PerceptionCLIP achieves better generalization, group robustness, and interoperability. Our code is available at https://github.com/umd-huang-lab/perceptionCLIP