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Convolutional Visual Prompt for Robust Visual Perception

arXiv 2303.00198 ↗First posted 1 Mar 2023cs.CV cs.LGcited by 17

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Abstract (arXiv)

Vision models are often vulnerable to out-of-distribution (OOD) samples without adapting. While visual prompts offer a lightweight method of input-space adaptation for large-scale vision models, they rely on a high-dimensional additive vector and labeled data. This leads to overfitting when adapting models in a self-supervised test-time setting without labels. We introduce convolutional visual prompts (CVP) for label-free test-time adaptation for robust visual perception. The structured nature of CVP demands fewer trainable parameters, less than 1% compared to standard visual prompts, combating overfitting. Extensive experiments and analysis on a wide variety of OOD visual perception tasks show that our approach is effective, improving robustness by up to 5.87% over several large-scale models.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 56 of its 68 references that are in the map. First posted on arXiv 1 Mar 2023.

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56 of 68 references.

References · 56

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Citing papers per year: 2023: 1; 2024: 9; 2025: 5; 2026: 2.

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  1. 2025Parameter-Efficient Fine-Tuning for Foundation Modelscited by 43
  2. 2024Convolutional Prompting meets Language Models for Continual Learningcited by 32
  3. 2023AutoVP: An Automated Visual Prompting Framework and Benchmarkcited by 29
  4. 2024Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmarkcited by 21
  5. 2025DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformerscited by 17
  6. 2024ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shiftscited by 16
  7. 2024Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognitioncited by 16
  8. 2025Advancing Prompt-Based Methods for Replay-Independent General Continual Learningcited by 9
  9. 2025Test-Time Visual In-Context Tuningcited by 7
  10. 2024Test-time Adaptation Meets Image Enhancement: Improving Accuracy via Uncertainty-aware Logit Switchingcited by 2
  11. 2024Proactive Schemes: A Survey of Adversarial Attacks for Social Goodcited by 2
  12. 2026ClipTTT: CLIP-Guided Test-Time Training Helps LVLMs See Bettercited by 2
5 more
  1. 2024Convolutional Prompting for Broad-Domain Retinal Vessel Segmentationcited by 1
  2. 2024Differentiable Prompt Learning for Vision Language Modelscited by 1
  3. 2024High-Resolution Be Aware! Improving the Self-Supervised Real-World Super-Resolutioncited by 0
  4. 2025Enhancing Visual Prompting through Expanded Transformation Space and Overfitting Mitigationcited by 0
  5. 2026Spike-NVPT: Learning Robust Visual Prompts via Bio-Inspired Temporal Filtering and Discretizationcited by 0