no code implementations • 21 Apr 2024 • Liheng Ma, Soumyasundar Pal, Yitian Zhang, Jiaming Zhou, Yingxue Zhang, Mark Coates
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified.
1 code implementation • 14 Mar 2024 • Yitian Zhang, Yue Bai, Huan Wang, Yizhou Wang, Yun Fu
Current training pipelines in object recognition neglect Hue Jittering when doing data augmentation as it not only brings appearance changes that are detrimental to classification, but also the implementation is inefficient in practice.
2 code implementations • 7 Nov 2023 • Yitian Zhang, Liheng Ma, Soumyasundar Pal, Yingxue Zhang, Mark Coates
Recent architectures learn complex temporal patterns by segmenting a time-series into patches and using the patches as tokens.
2 code implementations • CVPR 2023 • Yitian Zhang, Yue Bai, Chang Liu, Huan Wang, Sheng Li, Yun Fu
To fix this issue, we propose a general framework, named Frame Flexible Network (FFN), which not only enables the model to be evaluated at different frames to adjust its computation, but also reduces the memory costs of storing multiple models significantly.
1 code implementation • 18 Nov 2022 • Yitian Zhang, Yue Bai, Huan Wang, Yi Xu, Yun Fu
To tackle this problem, we propose Ample and Focal Network (AFNet), which is composed of two branches to utilize more frames but with less computation.
1 code implementation • 13 Oct 2022 • Yue Bai, Huan Wang, Xu Ma, Yitian Zhang, Zhiqiang Tao, Yun Fu
We validate the potential of PEMN learning masks on random weights with limited unique values and test its effectiveness for a new compression paradigm based on different network architectures.
no code implementations • 21 Sep 2022 • Yitian Zhang, Florence Regol, Antonios Valkanas, Mark Coates
We propose a framework called GraphTNC for unsupervised learning of joint representations of the graph and the time-series.