Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Lin, Chenguo"'
Comprehending natural language instructions is a charming property for both 2D and 3D layout synthesis systems. Existing methods implicitly model object joint distributions and express object relations, hindering generation's controllability. We intr
Externí odkaz:
http://arxiv.org/abs/2407.07580
Autor:
Pan, Panwang, Su, Zhuo, Lin, Chenguo, Fan, Zhen, Zhang, Yongjie, Li, Zeming, Shen, Tingting, Mu, Yadong, Liu, Yebin
Despite recent advancements in high-fidelity human reconstruction techniques, the requirements for densely captured images or time-consuming per-instance optimization significantly hinder their applications in broader scenarios. To tackle these issue
Externí odkaz:
http://arxiv.org/abs/2406.12459
Autor:
Lin, Chenguo, Mu, Yadong
Comprehending natural language instructions is a charming property for 3D indoor scene synthesis systems. Existing methods directly model object joint distributions and express object relations implicitly within a scene, thereby hindering the control
Externí odkaz:
http://arxiv.org/abs/2402.04717
Recent research on time-series self-supervised models shows great promise in learning semantic representations. However, it has been limited to small-scale datasets, e.g., thousands of temporal sequences. In this work, we make key technical contribut
Externí odkaz:
http://arxiv.org/abs/2310.07402
Publikováno v:
International Conference on Multimedia and Expo (ICME) 2021
Digital watermarking has been widely used to protect the copyright and integrity of multimedia data. Previous studies mainly focus on designing watermarking techniques that are robust to attacks of destroying the embedded watermarks. However, the eme
Externí odkaz:
http://arxiv.org/abs/2103.12489
Publikováno v:
International Joint Conferences on Artificial Intelligence (IJCAI) 2021, survey track
The intriguing phenomenon of adversarial examples has attracted significant attention in machine learning and what might be more surprising to the community is the existence of universal adversarial perturbations (UAPs), i.e. a single perturbation to
Externí odkaz:
http://arxiv.org/abs/2103.01498
Data hiding is the art of concealing messages with limited perceptual changes. Recently, deep learning has enriched it from various perspectives with significant progress. In this work, we conduct a brief yet comprehensive review of existing literatu
Externí odkaz:
http://arxiv.org/abs/2103.01607
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