A training-free solution for the 5th Dat-aCV Challenge @ CVPR 2026, Task II: Real-World Visual Illusions and Anomalies, which demonstrates that targeted preprocessing can substantially mitigate the perceptual biases of current vision-language models without any gradient updates.
VDiff-Bench provides a targeted diagnostic for evaluating comparative visual understanding in MLLMs, exposing failures that are not captured by standard single-image vision-language tasks.
ReVA is proposed, a region-aware VQA model that employs a frozen CLIP ViT-L/14 Vision Transformer (ViT) and a Qwen2.5-7B-Instruct large language model (LLM) connected through a dual bridge that aligns both whole-image and region-level representations with the LLM's embedding space.
Opti3D is proposed, a training-free, plug-and-play structured evidence construction framework that retrieves compact query-relevant visual evidence for off-the-shelf Video LLMs and shows that explicit geometry-aware view deduplication reduces redundant visual inputs and provides more reliable evidence for viewpoint-amb...
Q-CueGraph maps a question and an image representation to budgeted, coordinate-level observations for a frozen reader, and reaches 92% of full-image ANLS on InfographicVQA from about half the image area.
An empirical study comparing two modular pipelines with SA-DBNet, a custom detector architecture combining ResNet-18 with self-attention spatial modeling and deformable convolutions against an end-to-end vision-language baseline, evaluated on 4013 degraded images with 7000 question-answer pairs finds that conventional...
Ritali Vatsi, R. Jagadeesh, S. Baghel et al.· 0 citations
Three new vision-centric evaluation benchmarks in temporal frame retrieval, video future prediction, and causal memory distortion are introduced, all designed around evaluating visual understanding capabilities in large video models, which complements the existing approaches to evaluate video understanding in frontier...
Rwiddhi Chakraborty, Y. Wang, Cheng Zhang et al.· 0 citations
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