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cs.CV2024
EventCrab: Harnessing Frame and Point Synergy for Event-based Action Recognition and Beyond
Meiqi Cao, Xiangbo Shu, Jiachao Zhang +3
Event-based Action Recognition (EAR) possesses the advantages of high-temporal resolution capturing and privacy preservation compared with traditional action recognition. Current l…
cs.LG2024★ 1 cited
FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data
Binqian Xu, Xiangbo Shu, Haiyang Mei +3
Multimodal Large Language Models (MLLMs) have made significant advancements, demonstrating powerful capabilities in processing and understanding multimodal data. Fine-tuning MLLMs…
cs.CV2024
Locality-aware Cross-modal Correspondence Learning for Dense Audio-Visual Events Localization
Ling Xing, Hongyu Qu, Rui Yan +2
Dense-localization Audio-Visual Events (DAVE) aims to identify time boundaries and corresponding categories for events that are both audible and visible in a long video, where even…