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cs.CV2026

LENS: Adaptive Spatio-Temporal Zooming for Keyframe Sampling in Long-Form Videos

Ce Zhang, Jinxi He, Katia Sycara +1

Despite rapid progress in Multi-modal Large Language Models (MLLMs), understanding long-form videos is still bottlenecked by limited context windows. While recent keyframe sampling…

cs.CV2026

Goal2Pixel: Grounding Goals to Pixels for Vision-Language Navigation

Muyi Bao, Yuxin Cai, Hang Xu +7

Vision-language models (VLMs) have become a common foundation for vision-and-language navigation in continuous environments (VLN-CE). Yet most VLM-based methods cast navigation as…

cs.CV2026

Evolving Contextual Safety in Multi-Modal Large Language Models via Inference-Time Self-Reflective Memory

Ce Zhang, Jinxi He, Junyi He +2

Multi-modal Large Language Models (MLLMs) have achieved remarkable performance across a wide range of visual reasoning tasks, yet their vulnerability to safety risks remains a pres…

cs.CV2026

ROAP: A Reading-Order and Attention-Prior Pipeline for Optimizing Layout Transformers in Key Information Extraction

Tingwei Xie, Jinxin He, Yonghong Song

The efficacy of Multimodal Transformers in visually-rich document understanding (VrDU) is critically constrained by two inherent limitations: the lack of explicit modeling for logi…

cs.CV2025

Caption Anything in Video: Fine-grained Object-centric Captioning via Spatiotemporal Multimodal Prompting

Yunlong Tang, Jing Bi, Chao Huang +16

We present CAT-V (Caption AnyThing in Video), a training-free framework for fine-grained object-centric video captioning that enables detailed descriptions of user-selected objects…

cs.CV2025

VERIFY: A Benchmark of Visual Explanation and Reasoning for Investigating Multimodal Reasoning Fidelity

Jing Bi, Junjia Guo, Susan Liang +8

Visual reasoning is central to human cognition, enabling individuals to interpret and abstractly understand their environment. Although recent Multimodal Large Language Models (MLL…