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pro vyhledávání: '"Li, Siyuan"'
Autor:
Li, Siyuan, Ma, Zhe, Liu, Feifan, Lu, Jiani, Xiao, Qinqin, Sun, Kewu, Cui, Lingfei, Yang, Xirui, Liu, Peng, Wang, Xun
Robot task planning is an important problem for autonomous robots in long-horizon challenging tasks. As large pre-trained models have demonstrated superior planning ability, recent research investigates utilizing large models to achieve autonomous pl
Externí odkaz:
http://arxiv.org/abs/2411.06920
Autor:
Tan, Cheng, Cao, Zhenxiao, Gao, Zhangyang, Wu, Lirong, Li, Siyuan, Huang, Yufei, Xia, Jun, Hu, Bozhen, Li, Stan Z.
Post-translational modifications (PTMs) profoundly expand the complexity and functionality of the proteome, regulating protein attributes and interactions that are crucial for biological processes. Accurately predicting PTM sites and their specific t
Externí odkaz:
http://arxiv.org/abs/2411.01856
Autor:
Wang, Yongpan, Li, Hong, Zhu, Xiaojie, Li, Siyuan, Dong, Chaopeng, Yang, Shouguo, Qin, Kangyuan
Binary code search plays a crucial role in applications like software reuse detection. Currently, existing models are typically based on either internal code semantics or a combination of function call graphs (CG) and internal code semantics. However
Externí odkaz:
http://arxiv.org/abs/2411.01102
The field of face recognition (FR) has undergone significant advancements with the rise of deep learning. Recently, the success of unsupervised learning and graph neural networks has demonstrated the effectiveness of data structure information. Consi
Externí odkaz:
http://arxiv.org/abs/2410.10587
Recent advancements in brain-computer interfaces (BCIs) have enabled the decoding of lexical tones from intracranial recordings, offering the potential to restore the communication abilities of speech-impaired tonal language speakers. However, data h
Externí odkaz:
http://arxiv.org/abs/2410.12866
Autor:
Li, Siyuan, Tian, Juanxi, Wang, Zedong, Zhang, Luyuan, Liu, Zicheng, Jin, Weiyang, Liu, Yang, Sun, Baigui, Li, Stan Z.
This paper delves into the interplay between vision backbones and optimizers, unvealing an inter-dependent phenomenon termed \textit{\textbf{b}ackbone-\textbf{o}ptimizer \textbf{c}oupling \textbf{b}ias} (BOCB). We observe that canonical CNNs, such as
Externí odkaz:
http://arxiv.org/abs/2410.06373
Multiple object tracking in complex scenarios - such as coordinated dance performances, team sports, or dynamic animal groups - presents unique challenges. In these settings, objects frequently move in coordinated patterns, occlude each other, and ex
Externí odkaz:
http://arxiv.org/abs/2410.01806
The supervision of state-of-the-art multiple object tracking (MOT) methods requires enormous annotation efforts to provide bounding boxes for all frames of all videos, and instance IDs to associate them through time. To this end, we introduce Walker,
Externí odkaz:
http://arxiv.org/abs/2409.17221
Autor:
Li, Siyuan, Ke, Lei, Yang, Yung-Hsu, Piccinelli, Luigi, Segù, Mattia, Danelljan, Martin, Van Gool, Luc
Open-vocabulary Multiple Object Tracking (MOT) aims to generalize trackers to novel categories not in the training set. Currently, the best-performing methods are mainly based on pure appearance matching. Due to the complexity of motion patterns in t
Externí odkaz:
http://arxiv.org/abs/2409.11235
Autor:
Jin, Xin, Zhu, Hongyu, Li, Siyuan, Wang, Zedong, Liu, Zicheng, Yu, Chang, Qin, Huafeng, Li, Stan Z.
As Deep Neural Networks have achieved thrilling breakthroughs in the past decade, data augmentations have garnered increasing attention as regularization techniques when massive labeled data are unavailable. Among existing augmentations, Mixup and re
Externí odkaz:
http://arxiv.org/abs/2409.05202