6 citations · 9 across the 5 of their papers we have counts for
8 papers
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…
Enabling Timely Guidance before Skill Retrieval: Retaining Helpful Warm Tips in Agent Context
Feng Liang, Yupeng Li, Runhao Zeng +2
Reusable skills help LLM-based agents solve complex tasks, but the agent must receive guidance before it commits to an ineffective approach. Existing skill mechanisms often expose…
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…
FedSM: Robust Semantics-Guided Feature Mixup for Bias Reduction in Federated Learning with Long-Tail Data
Jingrui Zhang, Yimeng Xu, Shujie Li +5
Federated Learning (FL) enables collaborative model training across decentralized clients without sharing private data. However, FL suffers from biased global models due to non-IID…
A hierarchy tree data structure for behavior-based user segment representation
Yang Liu, Xuejiao Kang, Sathya Iyer +10
User attributes are essential in multiple stages of modern recommendation systems and are particularly important for mitigating the cold-start problem and improving the experience…
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…