most citedLoRA in LoRA: Towards Parameter-Efficient Architecture Expansion for Continual Visual Instruction Tuning

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cs.CV2026

Learning to Use Imagination: Progress-Conditioned Future Utilization for World Action Models

Yijie Zhu, Zitong Yu, Wei Li +4

World Action Models (WAMs) extend Vision-Language-Action (VLA) models by incorporating future visual dynamics into action generation. However, existing WAMs often utilize imagined…

cs.CV2026

PhysAgent: A Multi-Agent Framework for Reliable Remote Heart Rate Estimation

Yehui Yang, Bo Zhao, Junzhe Cao +4

Remote photoplethysmography (rPPG) enables non-contact heart-rate estimation from facial videos, but its weak physiological signal is easily corrupted by motion, illumination chang…

cs.CV2026

GMoT: Gated Motion-Aware Tokenization for Fine-Grained Micro-Gesture Video Reasoning with Multimodal LLMs

Taorui Wang, Wei Xia, Hui Ma +5

Micro-gesture recognition demands the detection of fleeting, spatially localized movements that are frequently overwhelmed by dominant static appearances and background noise. Whil…

cs.CV2026

StrLoRA: Towards Streaming Continual Visual Instruction Tuning for MLLMs

Chang Che, Ziqi Wang, Hui Ma +2

Continual Visual Instruction Tuning (CVIT) enables Multimodal Large Language Models to incrementally acquire new abilities. However, existing CVIT methods operate under a restricti…

cs.CV2026

AffectAgent: Collaborative Multi-Agent Reasoning for Retrieval-Augmented Multimodal Emotion Recognition

Zeheng Wang, Zitong Yu, Yijie Zhu +9

LLM-based multimodal emotion recognition relies on static parametric memory and often hallucinates when interpreting nuanced affective states. In this paper, given that single-roun…

cs.CV2025

Harmonious Parameter Adaptation in Continual Visual Instruction Tuning for Safety-Aligned MLLMs

Ziqi Wang, Chang Che, Qi Wang +4

While continual visual instruction tuning (CVIT) has shown promise in adapting multimodal large language models (MLLMs), existing studies predominantly focus on models without safe…