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HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution
Xiang Zhang, Yulun Zhang, Fisher Yu
Transformers have exhibited promising performance in computer vision tasks including image super-resolution (SR). However, popular transformer-based SR methods often employ window…
Matching Anything by Segmenting Anything
Siyuan Li, Lei Ke, Martin Danelljan +4
The robust association of the same objects across video frames in complex scenes is crucial for many applications, especially Multiple Object Tracking (MOT). Current methods predom…
UniDepth: Universal Monocular Metric Depth Estimation
Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis +4
Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks in 3D perception and modeling. However, the remarkable accuracy of recent MMDE methods is c…
DexDribbler: Learning Dexterous Soccer Manipulation via Dynamic Supervision
Yutong Hu, Kehan Wen, Fisher Yu
Learning dexterous locomotion policy for legged robots is becoming increasingly popular due to its ability to handle diverse terrains and resemble intelligent behaviors. However, j…
SM-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving
Zhiyuan Wu, Yi Feng, Chuang-Wei Liu +3
Semantic segmentation and stereo matching are two essential components of 3D environmental perception systems for autonomous driving. Nevertheless, conventional approaches often ad…
ICGNet: A Unified Approach for Instance-Centric Grasping
René Zurbrügg, Yifan Liu, Francis Engelmann +4
Accurate grasping is the key to several robotic tasks including assembly and household robotics. Executing a successful grasp in a cluttered environment requires multiple levels of…