Zobrazeno 1 - 10
of 2 376
pro vyhledávání: '"Yang, Yuting"'
Autor:
Cioppa, Anthony, Giancola, Silvio, Somers, Vladimir, Joos, Victor, Magera, Floriane, Held, Jan, Ghasemzadeh, Seyed Abolfazl, Zhou, Xin, Seweryn, Karolina, Kowalczyk, Mateusz, Mróz, Zuzanna, Łukasik, Szymon, Hałoń, Michał, Mkhallati, Hassan, Deliège, Adrien, Hinojosa, Carlos, Sanchez, Karen, Mansourian, Amir M., Miralles, Pierre, Barnich, Olivier, De Vleeschouwer, Christophe, Alahi, Alexandre, Ghanem, Bernard, Van Droogenbroeck, Marc, Gorski, Adam, Clapés, Albert, Boiarov, Andrei, Afanasiev, Anton, Xarles, Artur, Scott, Atom, Lim, ByoungKwon, Yeung, Calvin, Gonzalez, Cristian, Rüfenacht, Dominic, Pacilio, Enzo, Deuser, Fabian, Altawijri, Faisal Sami, Cachón, Francisco, Kim, HanKyul, Wang, Haobo, Choe, Hyeonmin, Kim, Hyunwoo J, Kim, Il-Min, Kang, Jae-Mo, Tursunboev, Jamshid, Yang, Jian, Hong, Jihwan, Lee, Jimin, Zhang, Jing, Lee, Junseok, Zhang, Kexin, Habel, Konrad, Jiao, Licheng, Li, Linyi, Gutiérrez-Pérez, Marc, Ortega, Marcelo, Li, Menglong, Lopatto, Milosz, Kasatkin, Nikita, Nemtsev, Nikolay, Oswald, Norbert, Udin, Oleg, Kononov, Pavel, Geng, Pei, Alotaibi, Saad Ghazai, Kim, Sehyung, Ulasen, Sergei, Escalera, Sergio, Zhang, Shanshan, Yang, Shuyuan, Moon, Sunghwan, Moeslund, Thomas B., Shandyba, Vasyl, Golovkin, Vladimir, Dai, Wei, Chung, WonTaek, Liu, Xinyu, Zhu, Yongqiang, Kim, Youngseo, Li, Yuan, Yang, Yuting, Xiao, Yuxuan, Cheng, Zehua, Li, Zhihao
The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field unde
Externí odkaz:
http://arxiv.org/abs/2409.10587
The success of speech-image retrieval relies on establishing an effective alignment between speech and image. Existing methods often model cross-modal interaction through simple cosine similarity of the global feature of each modality, which fall sho
Externí odkaz:
http://arxiv.org/abs/2408.13705
Autor:
Ding, Henghui, Liu, Chang, Wei, Yunchao, Ravi, Nikhila, He, Shuting, Bai, Song, Torr, Philip, Miao, Deshui, Li, Xin, He, Zhenyu, Wang, Yaowei, Yang, Ming-Hsuan, Xu, Zhensong, Yao, Jiangtao, Wu, Chengjing, Liu, Ting, Liu, Luoqi, Liu, Xinyu, Zhang, Jing, Zhang, Kexin, Yang, Yuting, Jiao, Licheng, Yang, Shuyuan, Gao, Mingqi, Luo, Jingnan, Yang, Jinyu, Han, Jungong, Zheng, Feng, Cao, Bin, Zhang, Yisi, Lin, Xuanxu, He, Xingjian, Zhao, Bo, Liu, Jing, Pan, Feiyu, Fang, Hao, Lu, Xiankai
Pixel-level Video Understanding in the Wild Challenge (PVUW) focus on complex video understanding. In this CVPR 2024 workshop, we add two new tracks, Complex Video Object Segmentation Track based on MOSE dataset and Motion Expression guided Video Seg
Externí odkaz:
http://arxiv.org/abs/2406.17005
Autor:
Fu, Ying, Li, Yu, You, Shaodi, Shi, Boxin, Chen, Linwei, Zou, Yunhao, Wang, Zichun, Li, Yichen, Han, Yuze, Zhang, Yingkai, Wang, Jianan, Liu, Qinglin, Yu, Wei, Lv, Xiaoqian, Li, Jianing, Zhang, Shengping, Ji, Xiangyang, Chen, Yuanpei, Zhang, Yuhan, Peng, Weihang, Zhang, Liwen, Xu, Zhe, Gou, Dingyong, Li, Cong, Xu, Senyan, Zhang, Yunkang, Jiang, Siyuan, Lu, Xiaoqiang, Jiao, Licheng, Liu, Fang, Liu, Xu, Li, Lingling, Ma, Wenping, Yang, Shuyuan, Xie, Haiyang, Zhao, Jian, Huang, Shihua, Cheng, Peng, Shen, Xi, Wang, Zheng, An, Shuai, Zhu, Caizhi, Li, Xuelong, Zhang, Tao, Li, Liang, Liu, Yu, Yan, Chenggang, Zhang, Gengchen, Jiang, Linyan, Song, Bingyi, An, Zhuoyu, Lei, Haibo, Luo, Qing, Song, Jie, Liu, Yuan, Li, Qihang, Zhang, Haoyuan, Wang, Lingfeng, Chen, Wei, Luo, Aling, Li, Cheng, Cao, Jun, Chen, Shu, Dou, Zifei, Liu, Xinyu, Zhang, Jing, Zhang, Kexin, Yang, Yuting, Gou, Xuejian, Wang, Qinliang, Liu, Yang, Zhao, Shizhan, Zhang, Yanzhao, Yan, Libo, Guo, Yuwei, Li, Guoxin, Gao, Qiong, Che, Chenyue, Sun, Long, Chen, Xiang, Li, Hao, Pan, Jinshan, Xie, Chuanlong, Chen, Hongming, Li, Mingrui, Deng, Tianchen, Huang, Jingwei, Li, Yufeng, Wan, Fei, Xu, Bingxin, Cheng, Jian, Liu, Hongzhe, Xu, Cheng, Zou, Yuxiang, Pan, Weiguo, Dai, Songyin, Jia, Sen, Zhang, Junpei, Chen, Puhua
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to inform and enhance deep learning models, we can develop more robust and ac
Externí odkaz:
http://arxiv.org/abs/2406.10744
Video Object Segmentation (VOS) is a vital task in computer vision, focusing on distinguishing foreground objects from the background across video frames. Our work draws inspiration from the Cutie model, and we investigate the effects of object memor
Externí odkaz:
http://arxiv.org/abs/2406.03668
We consider ML query processing in distributed systems where GPU-enabled workers coordinate to execute complex queries: a computing style often seen in applications that interact with users in support of image processing and natural language processi
Externí odkaz:
http://arxiv.org/abs/2402.17652
We study a discrete-time consumption-based capital asset pricing model under expectations-based reference-dependent preferences. More precisely, we consider an endowment economy populated by a representative agent who derives utility from current con
Externí odkaz:
http://arxiv.org/abs/2401.12856
Quantization replaces floating point arithmetic with integer arithmetic in deep neural network models, providing more efficient on-device inference with less power and memory. In this work, we propose a framework for formally verifying properties of
Externí odkaz:
http://arxiv.org/abs/2312.12679
Recently, to mitigate the confusion between different languages in code-switching (CS) automatic speech recognition (ASR), the conditionally factorized models, such as the language-aware encoder (LAE), explicitly disregard the contextual information
Externí odkaz:
http://arxiv.org/abs/2309.16178
Autor:
Yu, Xuanlong, Zuo, Yi, Wang, Zitao, Zhang, Xiaowen, Zhao, Jiaxuan, Yang, Yuting, Jiao, Licheng, Peng, Rui, Wang, Xinyi, Zhang, Junpei, Zhang, Kexin, Liu, Fang, Alcover-Couso, Roberto, SanMiguel, Juan C., Escudero-Viñolo, Marcos, Tian, Hanlin, Matsui, Kenta, Wang, Tianhao, Adan, Fahmy, Gao, Zhitong, He, Xuming, Bouniot, Quentin, Moghaddam, Hossein, Rai, Shyam Nandan, Cermelli, Fabio, Masone, Carlo, Pilzer, Andrea, Ricci, Elisa, Bursuc, Andrei, Solin, Arno, Trapp, Martin, Li, Rui, Yao, Angela, Chen, Wenlong, Simpson, Ivor, Campbell, Neill D. F., Franchi, Gianni
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segmentation in urban environments, with a particular focus on natural advers
Externí odkaz:
http://arxiv.org/abs/2309.15478