Zobrazeno 1 - 10
of 5 441
pro vyhledávání: '"Tang, Jin"'
Autor:
Jiang, Zequn, Ma, Yanxia, Tian, Tian, Sun, Yan, Chen, Hao, Lu, Ye, Wu, Yan, Jiang, Haiying, Li, Wenting, Li, Li, Zhou, Hongguang, Wu, Mianhua
Publikováno v:
In Journal of Ethnopharmacology 10 August 2020 258
Autor:
Sun, Tian-Rui, Geng, Jin-Jun, Yan, Jing-Zhi, Hu, You-Dong, Wu, Xue-Feng, Castro-Tirado, Alberto J., Yang, Chao, Ping, Yi-Ding, Hu, Chen-Ran, Xu, Fan, Gao, Hao-Xuan, Jiang, Ji-An, Zhu, Yan-Tian, Xue, Yongquan, Pérez-García, Ignacio, Wu, Si-Yu, Fernández-García, Emilio, Caballero-García, María D., Sánchez-Ramírez, Rubén, Guziy, Sergiy, Olivares, Ignacio, del Pulgar, Carlos Jesus Pérez, Castellón, A., Castillo, Sebastián, Xiong, Ding-Rong, Pandey, Shashi B., Hiriart, David, García-Segura, Guillermo, Lee, William H., Carrasco-García, I. M., Park, Il H., Meintjes, Petrus J., van Heerden, Hendrik J., Martín-Carrillo, Antonio, Hanlon, Lorraine, Zhang, Bin-Bin, Maury, Alain, Hernández-García, L., Gritsevich, Maria, Rossi, Andrea, Maiorano, Elisabetta, Cusano, Felice, D'Avanzo, Paolo, Ferro, Matteo, Melandri, Andrea, De Pasquale, Massimiliano, Brivio, Riccardo, Fang, Min, Fan, Lu-Lu, Hu, Wei-Da, Wan, Zhen, Hu, Lei, Zuo, Ying-Xi, Tang, Jin-Long, Zhang, Xiao-Ling, Zheng, Xian-Zhong, Li, Bin, Luo, Wen-Tao, Liu, Wei, Wang, Jian, Zhang, Hong-Fei, Liu, Hao, Gao, Jie, Liang, Ming, Wang, Hai-Ren, Yao, Da-Zhi, Cheng, Jing-Quan, Zhao, Wen, Dai, Zi-Gao
Thanks to the rapidly increasing time-domain facilities, we are entering a golden era of research on gamma-ray bursts (GRBs). In this Letter, we report our observations of GRB 240529A with the Burst Optical Observer and Transient Exploring System, th
Externí odkaz:
http://arxiv.org/abs/2409.17983
Sound Event Detection (SED) plays a vital role in comprehending and perceiving acoustic scenes. Previous methods have demonstrated impressive capabilities. However, they are deficient in learning features of complex scenes from heterogeneous dataset.
Externí odkaz:
http://arxiv.org/abs/2409.06196
Existing vehicle detectors are usually obtained by training a typical detector (e.g., YOLO, RCNN, DETR series) on vehicle images based on a pre-trained backbone (e.g., ResNet, ViT). Some researchers also exploit and enhance the detection performance
Externí odkaz:
http://arxiv.org/abs/2408.13031
Existing RGBT tracking methods often design various interaction models to perform cross-modal fusion of each layer, but can not execute the feature interactions among all layers, which plays a critical role in robust multimodal representation, due to
Externí odkaz:
http://arxiv.org/abs/2408.08827
In the Sound Event Localization and Detection (SELD) task, Transformer-based models have demonstrated impressive capabilities. However, the quadratic complexity of the Transformer's self-attention mechanism results in computational inefficiencies. In
Externí odkaz:
http://arxiv.org/abs/2408.05057
The complementary benefits from visible and thermal infrared data are widely utilized in various computer vision task, such as visual tracking, semantic segmentation and object detection, but rarely explored in Multiple Object Tracking (MOT). In this
Externí odkaz:
http://arxiv.org/abs/2408.00969
The label annotations for chest X-ray image rib segmentation are time consuming and laborious, and the labeling quality heavily relies on medical knowledge of annotators. To reduce the dependency on annotated data, existing works often utilize genera
Externí odkaz:
http://arxiv.org/abs/2407.15903
Autor:
Wang, Xiao, Kong, Weizhe, Jin, Jiandong, Wang, Shiao, Gao, Ruichong, Ma, Qingchuan, Li, Chenglong, Tang, Jin
Current strong pedestrian attribute recognition models are developed based on Transformer networks, which are computationally heavy. Recently proposed models with linear complexity (e.g., Mamba) have garnered significant attention and have achieved a
Externí odkaz:
http://arxiv.org/abs/2407.10374
Autor:
Chen, Lan, Li, Dong, Wang, Xiao, Shao, Pengpeng, Zhang, Wei, Wang, Yaowei, Tian, Yonghong, Tang, Jin
Existing event stream-based pattern recognition models usually represent the event stream as the point cloud, voxel, image, etc., and design various deep neural networks to learn their features. Although considerable results can be achieved in simple
Externí odkaz:
http://arxiv.org/abs/2406.18845