1 citations · 1 across the 6 of their papers we have counts for
8 papers
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…
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…