no code implementations • 27 May 2024 • Kangye Ji, Fei Cheng, Zeqing Wang, Bohu Huang
Therefore, we employ only one network with the jump manner update to decouple the interplay and mine more semantic information from the loss for a more precise selection.
no code implementations • 18 Dec 2023 • Hui Fu, Zeqing Wang, Ke Gong, Keze Wang, Tianshui Chen, Haojie Li, Haifeng Zeng, Wenxiong Kang
Moreover, to facilitate disentangled representation learning, we introduce four well-designed constraints: an auxiliary style classifier, an auxiliary inverse classifier, a content contrastive loss, and a pair of latent cycle losses, which can effectively contribute to the construction of the identity-related style space and semantic-related content space.
no code implementations • 29 Nov 2023 • Zeqing Wang, Wentao Wan, Qiqing Lao, Runmeng Chen, Minjie Lang, Keze Wang, Liang Lin
Through this collaboration mechanism, our framework explicitly constructs an MVKB for a specific visual scene and reasons answers in a top-down reasoning process.
no code implementations • 18 Sep 2023 • Wentao Wan, Nan Kang, Zeqing Wang, Zhuojie Yang, Liang Lin, Keze Wang
Specifically, our CLVP distills the capabilities of well-trained task-specific models into the visual sub-modules in a stepwise and anti-forgetting manner.