Zobrazeno 1 - 10
of 611
pro vyhledávání: '"Chen, Yufan"'
Autor:
Peng, Kunyu, Wen, Di, Yang, Kailun, Luo, Ao, Chen, Yufan, Fu, Jia, Sarfraz, M. Saquib, Roitberg, Alina, Stiefelhagen, Rainer
In Open-Set Domain Generalization (OSDG), the model is exposed to both new variations of data appearance (domains) and open-set conditions, where both known and novel categories are present at test time. The challenges of this task arise from the dua
Externí odkaz:
http://arxiv.org/abs/2409.17555
Motivated by recent empirical findings on the periodic phenomenon of aggregated market volumes in equity markets, we aim to understand the causes and consequences of periodic trading activities through a game-theoretic perspective, examining market i
Externí odkaz:
http://arxiv.org/abs/2408.09505
Optical chemical structure recognition (OCSR) systems aim to extract the molecular structure information, usually in the form of molecular graph or SMILES, from images of chemical molecules. While many tools have been developed for this purpose, chal
Externí odkaz:
http://arxiv.org/abs/2407.18338
Autor:
Zheng, Junwei, Liu, Ruiping, Chen, Yufan, Peng, Kunyu, Wu, Chengzhi, Yang, Kailun, Zhang, Jiaming, Stiefelhagen, Rainer
Panoramic images, capturing a 360{\deg} field of view (FoV), encompass omnidirectional spatial information crucial for scene understanding. However, it is not only costly to obtain training-sufficient dense-annotated panoramas but also application-re
Externí odkaz:
http://arxiv.org/abs/2407.02685
Autor:
Peng, Kunyu, Fu, Jia, Yang, Kailun, Wen, Di, Chen, Yufan, Liu, Ruiping, Zheng, Junwei, Zhang, Jiaming, Sarfraz, M. Saquib, Stiefelhagen, Rainer, Roitberg, Alina
We introduce a new task called Referring Atomic Video Action Recognition (RAVAR), aimed at identifying atomic actions of a particular person based on a textual description and the video data of this person. This task differs from traditional action r
Externí odkaz:
http://arxiv.org/abs/2407.01872
Autor:
Peng, Zhaopeng, Fan, Xiaoliang, Chen, Yufan, Wang, Zheng, Pan, Shirui, Wen, Chenglu, Zhang, Ruisheng, Wang, Cheng
Adapting Foundation Models (FMs) for downstream tasks through Federated Learning (FL) emerges a promising strategy for protecting data privacy and valuable FMs. Existing methods fine-tune FM by allocating sub-FM to clients in FL, however, leading to
Externí odkaz:
http://arxiv.org/abs/2404.11536
Autor:
Chen, Yufan, Zhang, Jiaming, Peng, Kunyu, Zheng, Junwei, Liu, Ruiping, Torr, Philip, Stiefelhagen, Rainer
Before developing a Document Layout Analysis (DLA) model in real-world applications, conducting comprehensive robustness testing is essential. However, the robustness of DLA models remains underexplored in the literature. To address this, we are the
Externí odkaz:
http://arxiv.org/abs/2403.14442
Autor:
Dong, Cong, Yang, Jiahai, Li, Yun, Wu, Yue, Chen, Yufan, Li, Chenglong, Jiao, Haoran, Yin, Xia, Liu, Yuling
In recent years, DNS over Encrypted (DoE) methods have been regarded as a novel trend within the realm of the DNS ecosystem. In these DoE methods, DNS over HTTPS (DoH) provides encryption to protect data confidentiality while providing better obfusca
Externí odkaz:
http://arxiv.org/abs/2403.12363
Autor:
Xu, Yi, Peng, Kunyu, Wen, Di, Liu, Ruiping, Zheng, Junwei, Chen, Yufan, Zhang, Jiaming, Roitberg, Alina, Yang, Kailun, Stiefelhagen, Rainer
Understanding human actions from body poses is critical for assistive robots sharing space with humans in order to make informed and safe decisions about the next interaction. However, precise temporal localization and annotation of activity sequence
Externí odkaz:
http://arxiv.org/abs/2403.09975
In the field of chemical structure recognition, the task of converting molecular images into machine-readable data formats such as SMILES string stands as a significant challenge, primarily due to the varied drawing styles and conventions prevalent i
Externí odkaz:
http://arxiv.org/abs/2403.03691