3 citations · 3 across the 22 of their papers we have counts for
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PACE-MoE: Adaptive Domain Sampling and Domain-Specific LoRA for Medical Visual Grounding
Zhenxuan Wang, Jisheng Dang, Ronghao Lin +6
Medical visual grounding requires models to align clinical descriptions with spatial evidence across heterogeneous imaging domains. However, existing multi-domain post-training str…
AVC-Reward: Event-Centric Audio-Video Consistency Evaluation
Yuruo Wu, Kaihang Pan, Kai Shen +6
As joint audio-video generation models become more capable, they can generate audio-visual scenes exhibiting three forms of complexity: multi-event composition, multi-layer audio m…
DualAffect: Dual-Path Paralinguistic Integration for Generative Audiovisual Emotion Understanding
Yibo Wang, Jisheng Dang, Bimei Wang +8
Understanding human emotion is essential for multimodal systems that interpret both the content of an interaction and the speaker's expressive state. However, connecting paralingui…
EmoRSS: Mitigating Emotion-Induced Over-Refusal in Large Language Models
Shuyi Miao, Yaojin Ma, Chenhang Cui +5
Emotional expression can influence the safety decisions of large language models (LLMs), offering a potential avenue for improving safety alignment. Existing studies have mainly fo…
DNAlign: Dynamic Null-Space Safe Alignment for LLMs
Jisheng Dang, Yushuo Zhao, Dewei Liu +6
Ensuring the safe and reliable deployment of large language models (LLMs) remains a fundamental challenge. Existing safety alignment approaches either incur high computational cost…
Balancing Emotional Alignment and Semantic Consistency in Image Generation via Reinforcement Learning with Valence-Arousal Anchoring
Jisheng Dang, Zhenxuan Wang, Bin Li +3
Continuous emotion control in text-to-image generation requires a model to improve affective alignment without changing the objects, layout, or scene described by the prompt. Exist…
MVFA: A Multi-View Text-Guided Multimodal Fusion LLM Adapter for Sentiment Analysis and Emotion Recognition
Pengfei Shao, Jisheng Dang, Jiawen Fang +7
Multimodal sentiment analysis and emotion recognition in conversations demand effective modeling of heterogeneous interactions across textual, acoustic, and visual modalities. Alth…
Multi-Agent Self-Improving Reinforcement Learning for Video Reasoning
Mingwen Zhang, Jisheng Dang, Minqiang Yang +3
Video reasoning tasks such as grounded video question answering and temporal grounding require selecting temporal evidence that supports the query. In many current training setups,…
PhysMLLMs: Spatial Priors for Unified Referring Segmentation and Grounded Reasoning of Images and Videos
Siyao Yan, Bo Han, Jisheng Dang +7
Video multimodal large language models support language guided video segmentation, but they often show spatio temporal inconsistencies, e.g., jitter, drift, and identity switches.…
Read-Best Is Not Steer-Best: A Probing--Steering Layer Dissociation in Omni-Modal Large Language Models
Yibo Wang, Jisheng Dang, Bimei Wang +7
Omni-modal large language models integrate text, audio, and image signals into a shared residual stream, where concepts such as emotion can be linearly decoded and causally modifie…
AffectOmni: RL-Verifiable People-Centric Grounded Affective Reasoning for Social and Art-Related Scenes
Yibo Wang, Rui Yang, Jisheng Dang +7
Multimodal large language models (MLLMs) achieve strong performance on VQA and scene understanding, yet affective reasoning remains vulnerable to shortcut behavior. Models may pred…
SCOPE: Evolving Symbolic World for Planning in Open-Ended Environments
Yundaichuan Zhan, Minghe Gao, Zhongqi Yue +7
Recent works have explored integrating Vision-Language Models (VLMs) with classical planners that rely on symbolic representations of planning problems to generate long-horizon pla…
CARE: Competence-Aware Reward Shaping for Adaptive Reasoning Length in Video-MLLMs
Chengwen Liu, Hao Peng, Jisheng Dang +3
In multimodal video reasoning, reinforcement learning-based methods typically rely on simplistic and inflexible reasoning-length control strategies that fail to adapt to the model'…
Reasoning as Intersection: Consensus-Frame Alignment for Visual Focus in Video-MLLMs
Chengwen Liu, Zhe Huang, Jisheng Dang +3
Reinforcement learning has improved the reasoning ability of large language models, but applying outcome-only rewards to video multimodal large language models (Video-MLLMs) provid…
A Robust Point Cloud Analysis Framework Inspired By Primary Visual Cortex
Jisheng Dang, Dengyue Pan, Delin Deng +6
Despite significant advancements in point cloud analysis, reducing energy consumption and improving robustness remain understudied, largely due to the inherent limitations of Convo…
MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning
Shang Ma, Jisheng Dang, Wencan Zhang +6
We propose a multi-agent collaborative framework built upon a lightweight Multimodal Large Language Model (MLLM), specifically designed for social intelligence reasoning. A key fea…
On the Implicit Reward Overfitting and the Low-rank Dynamics in RLVR
Hao Ye, Jisheng Dang, Junfeng Fang +7
Recent extensive research has demonstrated that the enhanced reasoning capabilities acquired by models through Reinforcement Learning with Verifiable Rewards (RLVR) are primarily c…
Scale-Aware UAV-to-Satellite Cross-View Geo-Localization: A Semantic Geometric Approach
Yibin Ye, Shuo Chen, Kun Wang +5
Cross-View Geo-Localization (CVGL) between UAV imagery and satellite images plays a crucial role in target localization and UAV self-positioning. However, most existing methods rel…
Decoupling Motion and Geometry in 4D Gaussian Splatting
Yi Zhang, Yulei Kang, Jiangxin Sun +3
High-fidelity reconstruction of dynamic scenes is an important yet challenging problem. While recent 4D Gaussian Splatting (4DGS) has demonstrated the ability to model temporal dyn…
Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning
Chengwen Liu, Xiaomin Yu, Zhuoyue Chang +15
In real-world video question answering scenarios, videos often provide only localized visual cues, while verifiable answers are distributed across the open web; models therefore ne…