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pro vyhledávání: '"Chen, C. L. Philip."'
Integrating complementary information from different data modalities can yield representation with stronger expressive ability. However, data quality varies across multimodal samples, highlighting the need for learning reliable multimodal representat
Externí odkaz:
http://arxiv.org/abs/2412.14489
Time series anomaly detection (TSAD) has been a research hotspot in both academia and industry in recent years. Deep learning methods have become the mainstream research direction due to their excellent performance. However, new viewpoints have emerg
Externí odkaz:
http://arxiv.org/abs/2412.05498
Real-world applications of stereo matching, such as autonomous driving, place stringent demands on both safety and accuracy. However, learning-based stereo matching methods inherently suffer from the loss of geometric structures in certain feature ch
Externí odkaz:
http://arxiv.org/abs/2411.12426
Autor:
Weng, Haohan, Zhao, Zibo, Lei, Biwen, Yang, Xianghui, Liu, Jian, Lai, Zeqiang, Chen, Zhuo, Liu, Yuhong, Jiang, Jie, Guo, Chunchao, Zhang, Tong, Gao, Shenghua, Chen, C. L. Philip
We propose a compressive yet effective mesh representation, Blocked and Patchified Tokenization (BPT), facilitating the generation of meshes exceeding 8k faces. BPT compresses mesh sequences by employing block-wise indexing and patch aggregation, red
Externí odkaz:
http://arxiv.org/abs/2411.07025
Generating compact and sharply detailed 3D meshes poses a significant challenge for current 3D generative models. Different from extracting dense meshes from neural representation, some recent works try to model the native mesh distribution (i.e., a
Externí odkaz:
http://arxiv.org/abs/2405.16890
Underwater images often suffer from various issues such as low brightness, color shift, blurred details, and noise due to light absorption and scattering caused by water and suspended particles. Previous underwater image enhancement (UIE) methods hav
Externí odkaz:
http://arxiv.org/abs/2404.17936
Low-light remote sensing images generally feature high resolution and high spatial complexity, with continuously distributed surface features in space. This continuity in scenes leads to extensive long-range correlations in spatial domains within rem
Externí odkaz:
http://arxiv.org/abs/2404.17400
Semi-supervised learning provides a solution to reduce the dependency of machine learning on labeled data. As one of the efficient semi-supervised techniques, self-training (ST) has received increasing attention. Several advancements have emerged to
Externí odkaz:
http://arxiv.org/abs/2404.12398
Autor:
Yuan, Haofeng, Zhu, Rongping, Yang, Wanlu, Song, Shiji, You, Keyou, Fan, Wei, Chen, C. L. Philip
The traveling purchaser problem (TPP) is an important combinatorial optimization problem with broad applications. Due to the coupling between routing and purchasing, existing works on TPPs commonly address route construction and purchase planning sim
Externí odkaz:
http://arxiv.org/abs/2404.02476
Autor:
Weng, Haohan, Huang, Danqing, Qiao, Yu, Hu, Zheng, Lin, Chin-Yew, Zhang, Tong, Chen, C. L. Philip
Templates serve as a good starting point to implement a design (e.g., banner, slide) but it takes great effort from designers to manually create. In this paper, we present Desigen, an automatic template creation pipeline which generates background im
Externí odkaz:
http://arxiv.org/abs/2403.09093