7 citations · 16 across the 15 of their papers we have counts for
15 papers
AffectSim: A Controllable Interactive 3D Simulation Benchmark for Embodied Affective Perception
Ke Xing, Zhilong Wang, Zheng Lian +8
Existing affective benchmarks largely rely on fixed recordings, where observation conditions are predetermined before inference. Consequently, they mainly evaluate passive affect r…
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…
AffectSeek: Agentic Affective Understanding in Long Videos under Vague User Queries
Zhen Zhang, Yuhang Yang, Yunxiang Jiang +5
Existing affective understanding studies have mainly focused on recognizing emotions from images, audio signals, or pre-cliped video clips, where the affective evidence is already…
Sparse Shortcuts: Facilitating Efficient Fusion in Multimodal Large Language Models
Jingrui Zhang, Feng Liang, Yong Zhang +3
With the remarkable success of large language models (LLMs) in natural language understanding and generation, multimodal large language models (MLLMs) have rapidly advanced in thei…
Revisiting Cross-Architecture Distillation: Adaptive Dual-Teacher Transfer for Lightweight Video Models
Ying Peng, Hongsen Ye, Changxin Huang +3
Vision Transformers (ViTs) have achieved strong performance in video action recognition, but their high computational cost limits their practicality. Lightweight CNNs are more effi…
A Byzantine-Robust Federated Learning with Nesterov Momentum
Lihan Xu, Xiaoyi Fan, Gang Wang +3
We investigate robust federated learning, where a group of workers collaboratively train a shared model under the orchestration of a central server in the presence of Byzantine adv…
CO-PFL: Contribution-Oriented Personalized Federated Learning for Heterogeneous Networks
Ke Xing, Yanjie Dong, Xiaoyi Fan +4
Personalized federated learning (PFL) addresses a critical challenge of collaboratively training customized models for clients with heterogeneous and scarce local data. Conventiona…
Emotion Recognition from Skeleton Data: A Comprehensive Survey
Haifeng Lu, Jiuyi Chen, Zhen Zhang +3
Emotion recognition through body movements has emerged as a compelling and privacy-preserving alternative to traditional methods that rely on facial expressions or physiological si…
Temporal Action Detection Model Compression by Progressive Block Drop
Xiaoyong Chen, Yong Guo, Jiaming Liang +3
Temporal action detection (TAD) aims to identify and localize action instances in untrimmed videos, which is essential for various video understanding tasks. However, recent improv…
Learning to Generate Gradients for Test-Time Adaptation via Test-Time Training Layers
Qi Deng, Shuaicheng Niu, Ronghao Zhang +4
Test-time adaptation (TTA) aims to fine-tune a trained model online using unlabeled testing data to adapt to new environments or out-of-distribution data, demonstrating broad appli…
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…
Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning
Runhao Zeng, Dingjie Zhou, Qiwei Liang +6
Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large languag…
Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey
Feng Liang, Zhen Zhang, Haifeng Lu +4
With rapidly increasing distributed deep learning workloads in large-scale data centers, efficient distributed deep learning framework strategies for resource allocation and worklo…
Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey
Feng Liang, Zhen Zhang, Haifeng Lu +3
With the rapid growth in the volume of data sets, models, and devices in the domain of deep learning, there is increasing attention on large-scale distributed deep learning. In con…
RelJoin: Relative-cost-based Selection of Distributed Join Methods for Query Plan Optimization
F. Liang, F. C. M. Lau, H. Cui +4
Selecting appropriate distributed join methods for logical join operations in a query plan is crucial for the performance of data-intensive scalable computing (DISC). Different net…