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cs.CV2026

RGBT-GroundBench: Visual Grounding Beyond RGB in Complex Real-World Scenarios

Tianyi Zhao, Jiawen Xi, Linhui Xiao +4

Visual grounding (VG) localizes target objects in an image from natural-language expressions. In real-world perception, RGB cues often degrade under low illumination and adverse we…

cs.CV2026

Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models

Xiaomin Yu, Yi Xin, Yuhui Zhang +12

Despite the success of multimodal contrastive learning in aligning visual and linguistic representations, a persistent geometric anomaly, the Modality Gap, remains: embeddings of d…

cs.CV2026

Moment-Video: Diagnosing Temporal Fidelity of Video MLLMs on Momentary Visual Events

Xiaolin Liu, Yilun Zhu, Xiangyu Zhao +9

Video multimodal large language models (MLLMs) have made rapid progress on general and long-form video understanding, yet their ability to preserve brief answer-critical visual evi…

cs.CV2026

GeoEyes: On-Demand Visual Focusing for Evidence-Grounded Understanding of Ultra-High-Resolution Remote Sensing Imagery

Fengxiang Wang, Mingshuo Chen, Yueying Li +10

The "thinking-with-images" paradigm enables multimodal large language models (MLLMs) to actively explore visual scenes via zoom-in tools. This is essential for ultra-high-resolutio…

cs.CV2026

RISE-Video: Can Video Generators Decode Implicit World Rules?

Mingxin Liu, Shuran Ma, Shibei Meng +9

While generative video models have achieved remarkable visual fidelity, their capacity to internalize and reason over implicit world rules remains a critical yet under-explored fro…

cs.CV2026

PhoStream: Benchmarking Real-World Streaming for Omnimodal Assistants in Mobile Scenarios

Xudong Lu, Huankang Guan, Yang Bo +10

Multimodal Large Language Models excel at offline audio-visual understanding, but their ability to serve as mobile assistants in continuous real-world streams remains underexplored…