arXiv Computer Vision
Don't Just Look, Intervene: Perturbation Based Region Labeling for VQA Images
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arXiv AI
Aug 11
Evidence-RL: Towards Evidence-intensive Visual Reasoning
arXiv:2608. 08021v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) should answer from concrete image evidence rather than language priors, dataset shortcuts, or irrelevant visual context.
arXiv Computer Vision
Sep 28
CCRV-Bench: Constraint-Based Evaluation of Causal Reasoning in Vision-Language Models
CCRV-Bench is a constraint‑driven benchmark designed to evaluate visual causal reasoning in vision‑language models on single‑image physical scenarios. It assesses four causal task dimensions—causal relation discovery, state prediction, causal diagnosis, and intervention—while applying constraints such as entity symbolization, spatial grounding, factual adversarial constraints, and minimalist output constraints to reduce shortcut learning. Experiments on 15 multimodal models reveal that constraint sensitivity varies by task and model, with intervention and spatial grounding having the largest impact and factual adversarial constraints improving causal diagnosis across models.
arXiv Computer Vision
Sep 22
Seeing is not Enough: Vision-Language Models Perceive Evidence but Fail to Act
arXiv:2609.22588v1 Announce Type: new Abstract: Vision-language models (VLMs) perform strongly on visual question answering benchmarks, yet often make decisions that contradict visual evidence they h...
arXiv AI
Jul 21
Seeing What Is Actually There: PriVE-Bench and PriVE-Tools for Counterfactual Evaluation of Agentic Visual Evidence in VLMs
arXiv:2607. 16311v1 Announce Type: cross Abstract: Vision-language models (VLMs) often answer visual questions using learned language and category priors rather than grounding their predictions in the image itself.
arXiv AI
Jun 11
From Prompts to Tokens: Internalizing Causal Supervision in Vision-Language Model for Multi-Image Causal Reasoning
arXiv:2606. 11745v1 Announce Type: cross Abstract: Visual causal reasoning is essential for understanding and intervening in the physical world, requiring identification of causal variables from visual inputs and reasoning over intervention effects.
arXiv AI
Jun 30
Be Faithful When Response: Returning Fluent and Grounded Answers for Vision-Language Models Reinforcement Learning
arXiv:2606. 29984v1 Announce Type: new Abstract: Reinforcement Learning (RL) is an important paradigm for improving the reasoning capabilities of Vision-Language Models (VLMs).