6 citations · 8 across the 8 of their papers we have counts for
8 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…
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…
MGHFT: Multi-Granularity Hierarchical Fusion Transformer for Cross-Modal Sticker Emotion Recognition
Jian Chen, Yuxuan Hu, Haifeng Lu +4
Although pre-trained visual models with text have demonstrated strong capabilities in visual feature extraction, sticker emotion understanding remains challenging due to its relian…
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…
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…
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…