48 citations · 113 across the 14 of their papers we have counts for
16 papers
Structure-aware Keypoint Localization for Videofluoroscopic Swallowing Study
Kai Zhou, Chuanshen Chen, Runhao Zeng +6
Videofluoroscopic Swallowing Study (VFSS) is one of the gold standard for diagnosing swallowing disorders, providing dynamic X-ray imaging of the swallowing process. Automated kine…
AffectReveal: Event-Grounded Emotion Recognition Beyond Visual Appearances
Yihao Qian, Runhao Zeng, Sicheng Zhao +3
Visual emotion recognition commonly assumes that all evidence required for prediction is contained in the observed image or video. Yet the same visible reaction can convey differen…
DepressionAgent: Reading, Listening, Seeing, and Deliberating Multimodal Evidence for Depression Risk Assessment
Fangjie Zhu, Haifeng Lu, Sicheng Zhao +2
Multimodal depression risk assessment requires jointly interpreting textual, acoustic, and visual cues that are often subtle, non-specific, context-dependent, and potentially incon…
Towards Stable Cross-Domain Depression Recognition under Missing Modalities
Jiuyi Chen, Mingkui Tan, Haifeng Lu +4
Depression poses serious public health risks, including suicide, underscoring the urgency of timely and scalable screening. Multimodal automatic depression detection (ADD) offers a…
Understanding Emotional Body Expressions via Large Language Models
Haifeng Lu, Jiuyi Chen, Feng Liang +3
Emotion recognition based on body movements is vital in human-computer interaction. However, existing emotion recognition methods predominantly focus on enhancing classification ac…
Towards Long Video Understanding via Fine-detailed Video Story Generation
Zeng You, Zhiquan Wen, Yaofo Chen +4
Long video understanding has become a critical task in computer vision, driving advancements across numerous applications from surveillance to content retrieval. Existing video und…
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning
Kunyang Lin, Yufeng Wang, Peihao Chen +4
Learning optimal behavior policy for each agent in multi-agent systems is an essential yet difficult problem. Despite fruitful progress in multi-agent reinforcement learning, the c…
Nav: Action-Aware Zero-Shot Robot Navigation by Exploiting Vision-and-Language Ability of Foundation Models
Peihao Chen, Xinyu Sun, Hongyan Zhi +5
We study the task of zero-shot vision-and-language navigation (ZS-VLN), a practical yet challenging problem in which an agent learns to navigate following a path described by langu…
Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation
Peihao Chen, Dongyu Ji, Kunyang Lin +4
We address a practical yet challenging problem of training robot agents to navigate in an environment following a path described by some language instructions. The instructions oft…
Graph Convolutional Module for Temporal Action Localization in Videos
Runhao Zeng, Wenbing Huang, Mingkui Tan +4
Temporal action localization has long been researched in computer vision. Existing state-of-the-art action localization methods divide each video into multiple action units (i.e.,…
RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning
Peihao Chen, Deng Huang, Dongliang He +5
We study unsupervised video representation learning that seeks to learn both motion and appearance features from unlabeled video only, which can be reused for downstream tasks such…
Location-aware Graph Convolutional Networks for Video Question Answering
Deng Huang, Peihao Chen, Runhao Zeng +3
We addressed the challenging task of video question answering, which requires machines to answer questions about videos in a natural language form. Previous state-of-the-art method…
Dense Regression Network for Video Grounding
Runhao Zeng, Haoming Xu, Wenbing Huang +3
We address the problem of video grounding from natural language queries. The key challenge in this task is that one training video might only contain a few annotated starting/endin…
A Thorough Comparison Study on Adversarial Attacks and Defenses for Common Thorax Disease Classification in Chest X-rays
Chendi Rao, Jiezhang Cao, Runhao Zeng +4
Recently, deep neural networks (DNNs) have made great progress on automated diagnosis with chest X-rays images. However, DNNs are vulnerable to adversarial examples, which may caus…
Graph Convolutional Networks for Temporal Action Localization
Runhao Zeng, Wenbing Huang, Mingkui Tan +4
Most state-of-the-art action localization systems process each action proposal individually, without explicitly exploiting their relations during learning. However, the relations b…
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction
Fengda Zhu, Xiaojun Chang, Runhao Zeng +1
Deep reinforcement learning has made significant progress in the field of continuous control, such as physical control and autonomous driving. However, it is challenging for a rein…