Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Li, Siyuan"'
The ability to recognize, localize and track dynamic objects in a scene is fundamental to many real-world applications, such as self-driving and robotic systems. Yet, traditional multiple object tracking (MOT) benchmarks rely only on a few object cat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97edc625a5a1c4d0a9448d9ad04cb31e
Autor:
Ye, Mingqiao, Ke, Lei, Li, Siyuan, Tai, Yu-Wing, Tang, Chi-Keung, Danelljan, Martin, Yu, Fisher
Object localization in general environments is a fundamental part of vision systems. While dominating on the COCO benchmark, recent Transformer-based detection methods are not competitive in diverse domains. Moreover, these methods still struggle to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1d48b8490c6785dbe9d4f1bac03c4078
Autor:
Zheng, Jiangbin, Wang, Yile, Tan, Cheng, Li, Siyuan, Wang, Ge, Xia, Jun, Chen, Yidong, Li, Stan Z.
Sign language recognition (SLR) is a weakly supervised task that annotates sign videos as textual glosses. Recent studies show that insufficient training caused by the lack of large-scale available sign datasets becomes the main bottleneck for SLR. M
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cbd8f3595fe22f2460b1ee7dfcac787a
Existing GAN inversion methods fail to provide latent codes for reliable reconstruction and flexible editing simultaneously. This paper presents a transformer-based image inversion and editing model for pretrained StyleGAN which is not only with less
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18d8474c7472de9ed3bb0792e4e73638
Spatiotemporal predictive learning aims to generate future frames by learning from historical frames. In this paper, we investigate existing methods and present a general framework of spatiotemporal predictive learning, in which the spatial encoder a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e2a697ee49f015e12c4ea660919ea8fe
Autor:
Li, Siyuan, Wang, Zedong, Liu, Zicheng, Tan, Cheng, Lin, Haitao, Wu, Di, Chen, Zhiyuan, Zheng, Jiangbin, Li, Stan Z.
Since the recent success of Vision Transformers (ViTs), explorations toward ViT-style architectures have triggered the resurgence of ConvNets. In this work, we explore the representation ability of modern ConvNets from a novel view of multi-order gam
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f9cb2ef27a2333beb3614ad1b47798e3
Data mixing augmentation have proved to be effective in improving the generalization ability of deep neural networks. While early methods mix samples by hand-crafted policies (e.g., linear interpolation), recent methods utilize saliency information t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aef8eff9a2f6a0275bdb8b9f800320a7
Autor:
Guo, Xiaojie, Li, Siyuan, Yu, Jinke, Zhang, Jiawan, Ma, Jiayi, Ma, Lin, Liu, Wei, Ling, Haibin
Being accurate, efficient, and compact is essential to a facial landmark detector for practical use. To simultaneously consider the three concerns, this paper investigates a neat model with promising detection accuracy under wild environments e.g., u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed12199b3dd9d4f786f9248856d343b0