Test-Time Visual In-Context Tuning

• URL: http://arxiv.org/abs/2503.21777v1
• Date: Thu, 27 Mar 2025 17:59:52 GMT
• Title: Test-Time Visual In-Context Tuning
• Authors: Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr, Federico Tombari, Bernt Schiele,
• Abstract summary: Visual in-context learning (VICL) allows the model to rapidly adapt to various tasks with only a handful of prompts and examples.<n>While effective, the existing VICL paradigm exhibits poor generalizability under distribution shifts.<n>We propose test-time Visual In-Context Tuning (VICT), a method that can adapt VICL models on the fly with a single test sample.
• Score: 85.62916644835902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual in-context learning (VICL), as a new paradigm in computer vision, allows the model to rapidly adapt to various tasks with only a handful of prompts and examples. While effective, the existing VICL paradigm exhibits poor generalizability under distribution shifts. In this work, we propose test-time Visual In-Context Tuning (VICT), a method that can adapt VICL models on the fly with a single test sample. Specifically, we flip the role between the task prompts and the test sample and use a cycle consistency loss to reconstruct the original task prompt output. Our key insight is that a model should be aware of a new test distribution if it can successfully recover the original task prompts. Extensive experiments on six representative vision tasks ranging from high-level visual understanding to low-level image processing, with 15 common corruptions, demonstrate that our VICT can improve the generalizability of VICL to unseen new domains. In addition, we show the potential of applying VICT for unseen tasks at test time. Code: https://github.com/Jiahao000/VICT.

Related papers

• Exploring Task-Level Optimal Prompts for Visual In-Context Learning [20.34945396590862]
  We propose task-level prompting to reduce the cost of searching for prompts during the inference stage.<n>We show that our proposed method can identify near-optimal prompts and reach the best VICL performance with a minimal cost.
  arXiv  Detail & Related papers  (2025-01-15T14:52:20Z)
• @Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology [31.779074930032184]
  Human-centered Assistive Technologies (ATs) for helping People with Visual Impairments (PVIs) are evolving into generalists, capable of performing multiple tasks simultaneously. We first create a novel AT benchmark (@Bench) guided by a pre-design user study with PVIs. Besides, we propose a novel AT model (@Model) that addresses all tasks simultaneously and can be expanded to more assistive functions for helping PVIs.
  arXiv  Detail & Related papers  (2024-09-21T18:30:17Z)
• Learning A Low-Level Vision Generalist via Visual Task Prompt [43.54563263106761]
  We propose a Visual task Prompt-based Image Processing (VPIP) framework to overcome these challenges. VPIP employs visual task prompts to manage tasks with different input-target domains and allows flexible selection of backbone network. Based on the VPIP framework, we train a low-level vision generalist model, namely GenLV, on 30 diverse tasks.
  arXiv  Detail & Related papers  (2024-08-16T08:37:56Z)
• In-context Prompt Learning for Test-time Vision Recognition with Frozen Vision-language Model [13.983810804606264]
  We propose In-Context Prompt Learning (InCPL) for test-time visual recognition tasks. InCPL associates a new test sample with very few labeled examples as context information. We introduce a context-aware unsupervised loss to optimize visual prompts tailored to test samples.
  arXiv  Detail & Related papers  (2024-03-10T08:15:51Z)
• Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model [83.85856356798531]
  VistaLLM is a visual system that addresses coarse- and fine-grained vision-language tasks. It employs a gradient-aware adaptive sampling technique to represent binary segmentation masks as sequences. We also introduce a novel task, AttCoSeg, which boosts the model's reasoning and grounding capability over multiple input images.
  arXiv  Detail & Related papers  (2023-12-19T18:53:01Z)
• Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models [76.410400238974]
  We propose TTA with feedback to rectify the model output and prevent the model from becoming blindly confident. A CLIP model is adopted as the reward model during TTA and provides feedback for the VLM. The proposed textitreinforcement learning with CLIP feedback(RLCF) framework is highly flexible and universal.
  arXiv  Detail & Related papers  (2023-05-29T11:03:59Z)
• Progressive Visual Prompt Learning with Contrastive Feature Re-formation [15.385630262368661]
  We propose a new Progressive Visual Prompt (ProVP) structure to strengthen the interactions among prompts of different layers. Our ProVP could effectively propagate the image embeddings to deep layers and behave partially similar to an instance adaptive prompt method. To the best of our knowledge, we are the first to demonstrate the superior performance of visual prompts in V-L models to previous prompt-based methods in downstream tasks.
  arXiv  Detail & Related papers  (2023-04-17T15:54:10Z)
• Exploring Efficient Few-shot Adaptation for Vision Transformers [70.91692521825405]
  We propose a novel efficient Transformer Tuning (eTT) method that facilitates finetuning ViTs in the Few-shot Learning tasks. Key novelties come from the newly presented Attentive Prefix Tuning (APT) and Domain Residual Adapter (DRA) We conduct extensive experiments to show the efficacy of our model.
  arXiv  Detail & Related papers  (2023-01-06T08:42:05Z)
• Images Speak in Images: A Generalist Painter for In-Context Visual Learning [98.78475432114595]
  In-context learning allows the model to rapidly adapt to various tasks with only a handful of prompts and examples. It is unclear how to define the general-purpose task prompts that the vision model can understand and transfer to out-of-domain tasks. We present Painter, a generalist model which redefines the output of core vision tasks as images, and specify task prompts as also images.
  arXiv  Detail & Related papers  (2022-12-05T18:59:50Z)
• Multitask Vision-Language Prompt Tuning [103.5967011236282]
  We propose multitask vision-language prompt tuning (MV) MV incorporates cross-task knowledge into prompt tuning for vision-language models. Results in 20 vision tasks demonstrate that the proposed approach outperforms all single-task baseline prompt tuning methods.
  arXiv  Detail & Related papers  (2022-11-21T18:41:44Z)
• Fine-grained Visual-Text Prompt-Driven Self-Training for Open-Vocabulary Object Detection [87.39089806069707]
  We propose a fine-grained Visual-Text Prompt-driven self-training paradigm for Open-Vocabulary Detection (VTP-OVD) During the adapting stage, we enable VLM to obtain fine-grained alignment by using learnable text prompts to resolve an auxiliary dense pixel-wise prediction task. Experiments show that our method achieves the state-of-the-art performance for open-vocabulary object detection, e.g., 31.5% mAP on unseen classes of COCO.
  arXiv  Detail & Related papers  (2022-11-02T03:38:02Z)
• A Practical Contrastive Learning Framework for Single-Image Super-Resolution [51.422185656787285]
  We investigate contrastive learning-based single image super-resolution from two perspectives. We propose a practical contrastive learning framework for SISR, named PCL-SR. Compared with existing benchmark methods, we re-train them by our proposed PCL-SR framework and achieve superior performance.
  arXiv  Detail & Related papers  (2021-11-27T15:42:12Z)
• Exploit Clues from Views: Self-Supervised and Regularized Learning for Multiview Object Recognition [66.87417785210772]
  This work investigates the problem of multiview self-supervised learning (MV-SSL) A novel surrogate task for self-supervised learning is proposed by pursuing "object invariant" representation. Experiments shows that the recognition and retrieval results using view invariant prototype embedding (VISPE) outperform other self-supervised learning methods.
  arXiv  Detail & Related papers  (2020-03-28T07:06:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.