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of 6
pro vyhledávání: '"Liu, Akide"'
Active learning enhances annotation efficiency by selecting the most revealing samples for labeling, thereby reducing reliance on extensive human input. Previous methods in semantic segmentation have centered on individual pixels or small areas, negl
Externí odkaz:
http://arxiv.org/abs/2408.13491
Autor:
Zhang, Zeyu, Liu, Akide, Chen, Qi, Chen, Feng, Reid, Ian, Hartley, Richard, Zhuang, Bohan, Tang, Hao
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 sequences effectively: (1) Current methods struggle to handle long mo
Externí odkaz:
http://arxiv.org/abs/2407.10061
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 generated tokens, significantly reducing the need for repetitive comput
Externí odkaz:
http://arxiv.org/abs/2405.14366
Human motion generation stands as a significant pursuit in generative computer vision, while achieving long-sequence and efficient motion generation remains challenging. Recent advancements in state space models (SSMs), notably Mamba, have showcased
Externí odkaz:
http://arxiv.org/abs/2403.07487
Autor:
Liu, Akide
Memory is identified as a crucial human faculty that allows for the retention of visual and linguistic information within the hippocampus and neurons in the brain, which can subsequently be retrieved to address real-world challenges that arise throug
Externí odkaz:
http://arxiv.org/abs/2307.12057
Autor:
Liu, Akide, Wang, Zihan
This competition focus on Urban-Sense Segmentation based on the vehicle camera view. Class highly unbalanced Urban-Sense images dataset challenge the existing solutions and further studies. Deep Conventional neural network-based semantic segmentation
Externí odkaz:
http://arxiv.org/abs/2206.12571