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20242026
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cs.CV2026

Seeing or Knowing? Visual Context Sensitivity in Multimodal Large Language Models

Jiaang Li, Chengzu Li, Zhaochong An +5

The paper investigates why multimodal large language models often ignore visual evidence, using image reconstruction and a new benchmark (WhatIfVis) to measure how well models bala…

cs.CV2026

Thinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning

Jiusong Ge, Yingkang Zhan, Wenjie Zhao +13

Traditional whole slide image (WSI) analysis methods typically rely on the multiple instance learning (MIL) paradigm, which extracts patch-level features at high magnification and…

cs.CV2026

How Well Do Models Follow Visual Instructions? VIBE: A Systematic Benchmark for Visual Instruction-Driven Image Editing

Huanyu Zhang, Xuehai Bai, Chengzu Li +9

Recent generative models have achieved remarkable progress in image editing. However, existing systems and benchmarks remain largely text-guided. In contrast, human communication i…

cs.CV2026

Video Understanding: From Geometry and Semantics to Unified Models

Zhaochong An, Zirui Li, Mingqiao Ye +9

Video understanding aims to enable models to perceive, reason about, and interact with the dynamic visual world. In contrast to image understanding, video understanding inherently…

cs.CV2026

Deforming Videos to Masks: Flow Matching for Referring Video Segmentation

Zanyi Wang, Dengyang Jiang, Liuzhuozheng Li +6

Referring Video Object Segmentation (RVOS) requires segmenting specific objects in a video guided by a natural language description. The core challenge of RVOS is to anchor abstrac…

cs.CV2025

Latent Sketchpad: Sketching Visual Thoughts to Elicit Multimodal Reasoning in MLLMs

Huanyu Zhang, Wenshan Wu, Chengzu Li +9

While Multimodal Large Language Models (MLLMs) excel at visual understanding, they often struggle in complex scenarios that require visual planning and imagination. Inspired by how…