Quo Vadis, Visual In-Context Learning? A Unified Benchmark Across Domains and Tasks

• URL: http://arxiv.org/abs/2606.10967v1
• Date: Tue, 09 Jun 2026 15:08:43 GMT
• Title: Quo Vadis, Visual In-Context Learning? A Unified Benchmark Across Domains and Tasks
• Abstract summary: We build a broad Visual In-Context BEnchmark (VIBE) with a focus on diverse imaging domains and a wide range of tasks.<n>We stress test six models on $14$ datasets and $12$ tasks and compare them under a unified, reproducible evaluation protocol.<n>Our evaluation uncovers key insights on the state of visual in-context learning, including limitations, systematic failure modes and promising directions.
• Score: 47.20669655453283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual in-context learning has been proposed as a pathway towards dynamic models that can generate predictions based on a provided context and thereby can adapt to new vision tasks at test-time. Yet, the evaluation of the adaptation capabilities of these models has been limited to narrow setups that mainly mirror tasks or image domains from pre-training for which real adaptation is not required. We address this gap by constructing a broad Visual In-Context BEnchmark (VIBE) with a focus on diverse imaging domains and a wide range of tasks. With this, we are able to get a much clearer picture of the adaptive capabilities of visual in-context models when faced with new image- and task distributions. We stress test six models on $14$ datasets and $12$ tasks (in total, we explore $106$ dataset-task combinations) and compare them under a unified, reproducible evaluation protocol, in an one-shot setting. Our evaluation uncovers key insights on the state of visual in-context learning, including limitations, systematic failure modes and promising directions. To foster broader evaluation, we will openly release our VIBE toolkit.

Related papers

• Beyond Model Size: Probing the Gaps in Visual in-Context Learning by Training a Tiny Model [11.54554255426278]
  We aim at making progress towards adaptive vision models, that can adapt to a new task at test-time.<n>We train a tiny visual in-context model with merely $1$ million parameters and a modest amount of $70,000$ images.<n>We compare the results of this severely capacity capped tiny model to $7,000times$ larger VICL models in different adaptive settings.
  arXiv  Detail & Related papers  (2026-06-09T14:13:58Z)
• How Well Do Models Follow Visual Instructions? VIBE: A Systematic Benchmark for Visual Instruction-Driven Image Editing [56.60465182650588]
  We introduce three-level interaction hierarchy that captures deictic grounding, morphological manipulation, and causal reasoning.<n>We propose a robust LMM-as-a-judge evaluation framework with task-specific metrics to enable scalable and fine-grained assessment.<n>We find that proprietary models exhibit early-stage visual instruction-following capabilities and consistently outperform open-source models.
  arXiv  Detail & Related papers  (2026-02-02T09:24:45Z)
• Test-Time Visual In-Context Tuning [85.62916644835902]
  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.
  arXiv  Detail & Related papers  (2025-03-27T17:59:52Z)
• Evaluation and Comparison of Visual Language Models for Transportation Engineering Problems [16.49637074299509]
  We have explored state-of-the-art vision language models (VLM) for vision-based transportation engineering tasks. The image classification task involves congestion detection and crack identification, whereas, for object detection, helmet violations were identified. We have applied open-source models such as CLIP, BLIP, OWL-ViT, Llava-Next, and closed-source GPT-4o to evaluate the performance of these VLM models.
  arXiv  Detail & Related papers  (2024-09-03T20:24:37Z)
• Multi-Modal Prompt Learning on Blind Image Quality Assessment [65.0676908930946]
  Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretraining model as their backbone to gain semantic awareness. Recent approaches have attempted to address this mismatch using prompt technology, but these solutions have shortcomings. This paper introduces an innovative multi-modal prompt-based methodology for IQA.
  arXiv  Detail & Related papers  (2024-04-23T11:45:32Z)
• FINEMATCH: Aspect-based Fine-grained Image and Text Mismatch Detection and Correction [66.98008357232428]
  We propose FineMatch, a new aspect-based fine-grained text and image matching benchmark. FineMatch focuses on text and image mismatch detection and correction. We show that models trained on FineMatch demonstrate enhanced proficiency in detecting fine-grained text and image mismatches.
  arXiv  Detail & Related papers  (2024-04-23T03:42:14Z)
• 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)
• IMProv: Inpainting-based Multimodal Prompting for Computer Vision Tasks [124.90137528319273]
  In this paper, we present IMProv, a generative model that is able to in-context learn visual tasks from multimodal prompts. We train a masked generative transformer on a new dataset of figures from computer vision papers and their associated captions. During inference time, we prompt the model with text and/or image task example(s) and have the model inpaint the corresponding output.
  arXiv  Detail & Related papers  (2023-12-04T09:48:29Z)
• Evaluating how interactive visualizations can assist in finding samples where and how computer vision models make mistakes [1.76602679361245]
  We present two interactive visualizations in the context of Sprite, a system for creating Computer Vision (CV) models. We study how these visualizations help Sprite's users identify (evaluate) and select (plan) images where a model is struggling and can lead to improved performance.
  arXiv  Detail & Related papers  (2023-05-19T14:43:00Z)
• 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)

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.