11 papers
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…
DeRelayL: Sustainable Decentralized Relay Learning
Haihan Duan, Tengfei Ma, Yuyang Qin +4
In the era of big data, large-scale machine learning models have revolutionized various fields, driving significant advancements. However, large-scale model training demands high f…
ZOTTA: Test-Time Adaptation with Gradient-Free Zeroth-Order Optimization
Ronghao Zhang, Shuaicheng Niu, Qi Deng +3
Test-time adaptation (TTA) aims to improve model robustness under distribution shifts by adapting to unlabeled test data, but most existing methods rely on backpropagation (BP), wh…
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…
Nesterov-Accelerated Robust Federated Learning Over Byzantine Adversaries
Lihan Xu, Yanjie Dong, 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…