Zobrazeno 1 - 10
of 18 644
pro vyhledávání: '"WEI, Yi"'
Click-Through Rate (CTR) prediction is essential in online advertising, where semantic information plays a pivotal role in shaping user decisions and enhancing CTR effectiveness. Capturing and modeling deep semantic information, such as a user's pref
Externí odkaz:
http://arxiv.org/abs/2412.06860
Attention-based architectures have become ubiquitous in time series forecasting tasks, including spatio-temporal (STF) and long-term time series forecasting (LTSF). Yet, our understanding of the reasons for their effectiveness remains limited. This w
Externí odkaz:
http://arxiv.org/abs/2410.24023
Combining sports and machine learning involves leveraging ML algorithms and techniques to extract insight from sports-related data such as player statistics, game footage, and other relevant information. However, datasets related to figure skating in
Externí odkaz:
http://arxiv.org/abs/2410.20427
Stereo matching for inland waterways is one of the key technologies for the autonomous navigation of Unmanned Surface Vehicles (USVs), which involves dividing the stereo images into reference images and target images for pixel-level matching. However
Externí odkaz:
http://arxiv.org/abs/2410.07915
For 6-DoF grasp detection, simulated data is expandable to train more powerful model, but it faces the challenge of the large gap between simulation and real world. Previous works bridge this gap with a sim-to-real way. However, this way explicitly o
Externí odkaz:
http://arxiv.org/abs/2410.06521
Autor:
Tan, Chaolei, Lin, Zihang, Pu, Junfu, Qi, Zhongang, Pei, Wei-Yi, Qu, Zhi, Wang, Yexin, Shan, Ying, Zheng, Wei-Shi, Hu, Jian-Fang
Video grounding is a fundamental problem in multimodal content understanding, aiming to localize specific natural language queries in an untrimmed video. However, current video grounding datasets merely focus on simple events and are either limited t
Externí odkaz:
http://arxiv.org/abs/2408.01669
Robotic grasping in clutters is a fundamental task in robotic manipulation. In this work, we propose an economic framework for 6-DoF grasp detection, aiming to economize the resource cost in training and meanwhile maintain effective grasp performance
Externí odkaz:
http://arxiv.org/abs/2407.08366
In this work, we introduce the Geometry-Aware Large Reconstruction Model (GeoLRM), an approach which can predict high-quality assets with 512k Gaussians and 21 input images in only 11 GB GPU memory. Previous works neglect the inherent sparsity of 3D
Externí odkaz:
http://arxiv.org/abs/2406.15333
Autor:
Wei, Yi-Lin, Jiang, Jian-Jian, Xing, Chengyi, Tan, Xian-Tuo, Wu, Xiao-Ming, Li, Hao, Cutkosky, Mark, Zheng, Wei-Shi
This paper explores a novel task "Dexterous Grasp as You Say" (DexGYS), enabling robots to perform dexterous grasping based on human commands expressed in natural language. However, the development of this field is hindered by the lack of datasets wi
Externí odkaz:
http://arxiv.org/abs/2405.19291
In this work, we propose a novel discriminative framework for dexterous grasp generation, named Dexterous Grasp TRansformer (DGTR), capable of predicting a diverse set of feasible grasp poses by processing the object point cloud with only one forward
Externí odkaz:
http://arxiv.org/abs/2404.18135