5 citations · 12 across the 14 of their papers we have counts for
6 papers · 1 filter
CIT: Rethinking Class-incremental Semantic Segmentation with a Class Independent Transformation
Jinchao Ge, Bowen Zhang, Akide Liu +4
Class-incremental semantic segmentation (CSS) requires that a model learn to segment new classes without forgetting how to segment previous ones: this is typically achieved by dist…
KMM: Key Frame Mask Mamba for Extended Motion Generation
Zeyu Zhang, Hang Gao, Akide Liu +9
Human motion generation is a cut-edge area of research in generative computer vision, with promising applications in video creation, game development, and robotic manipulation. The…
ESA: Annotation-Efficient Active Learning for Semantic Segmentation
Jinchao Ge, Zeyu Zhang, Minh Hieu Phan +4
Active learning enhances annotation efficiency by selecting the most revealing samples for labeling, thereby reducing reliance on extensive human input. Previous methods in semanti…
InfiniMotion: Mamba Boosts Memory in Transformer for Arbitrary Long Motion Generation
Zeyu Zhang, Akide Liu, Qi Chen +5
Text-to-motion generation holds potential for film, gaming, and robotics, yet current methods often prioritize short motion generation, making it challenging to produce long motion…
MiniCache: KV Cache Compression in Depth Dimension for Large Language Models
Akide Liu, Jing Liu, Zizheng Pan +3
A critical approach for efficiently deploying computationally demanding large language models (LLMs) is Key-Value (KV) caching. The KV cache stores key-value states of previously g…
Motion Mamba: Efficient and Long Sequence Motion Generation
Zeyu Zhang, Akide Liu, Ian Reid +3
Human motion generation stands as a significant pursuit in generative computer vision, while achieving long-sequence and efficient motion generation remains challenging. Recent adv…