1 code implementation • ECCV 2020 • Zhongzheng Ren, Zhiding Yu, Xiaodong Yang, Ming-Yu Liu, Alexander G. Schwing, Jan Kautz
Existing work on object detection often relies on a single form of annotation: the model is trained using either accurate yet costly bounding boxes or cheaper but less expressive image-level tags.
no code implementations • 4 Nov 2022 • Yuefan Wu, Zeyuan Chen, Shaowei Liu, Zhongzheng Ren, Shenlong Wang
Recovering the skeletal shape of an animal from a monocular video is a longstanding challenge.
1 code implementation • 4 Aug 2022 • Xiaoming Zhao, Yuan-Ting Hu, Zhongzheng Ren, Alexander G. Schwing
Specifically, a set of 3D locations within the view-frustum of the camera are first projected independently onto the image and a corresponding feature is subsequently extracted for each 3D location.
no code implementations • CVPR 2022 • Zhongzheng Ren, Aseem Agarwala, Bryan Russell, Alexander G. Schwing, Oliver Wang
We introduce an approach for selecting objects in neural volumetric 3D representations, such as multi-plane images (MPI) and neural radiance fields (NeRF).
1 code implementation • CVPR 2022 • Raymond A. Yeh, Yuan-Ting Hu, Zhongzheng Ren, Alexander G. Schwing
To study question (a), in this work, we propose total variation (TV) minimization as a layer for computer vision.
no code implementations • NeurIPS 2021 • Zhongzheng Ren, Xiaoming Zhao, Alexander G. Schwing
We introduce REDO, a class-agnostic framework to REconstruct the Dynamic Objects from RGBD or calibrated videos.
no code implementations • 6 Aug 2021 • Iou-Jen Liu, Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing
We evaluate `semantic tracklets' on the visual multi-agent particle environment (VMPE) and on the challenging visual multi-agent GFootball environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
1 code implementation • CVPR 2021 • Zhongzheng Ren, Ishan Misra, Alexander G. Schwing, Rohit Girdhar
We introduce WyPR, a Weakly-supervised framework for Point cloud Recognition, requiring only scene-level class tags as supervision.
no code implementations • 21 Oct 2020 • Zhongzheng Ren, Zhiding Yu, Xiaodong Yang, Ming-Yu Liu, Alexander G. Schwing, Jan Kautz
Existing work on object detection often relies on a single form of annotation: the model is trained using either accurate yet costly bounding boxes or cheaper but less expressive image-level tags.
no code implementations • NeurIPS 2020 • Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing
Existing semi-supervised learning (SSL) algorithms use a single weight to balance the loss of labeled and unlabeled examples, i. e., all unlabeled examples are equally weighted.
2 code implementations • CVPR 2020 • Zhongzheng Ren, Zhiding Yu, Xiaodong Yang, Ming-Yu Liu, Yong Jae Lee, Alexander G. Schwing, Jan Kautz
Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training.
Ranked #1 on
Weakly Supervised Object Detection
on COCO test-dev
1 code implementation • ECCV 2018 • Zhongzheng Ren, Yong Jae Lee, Michael S. Ryoo
The end result is a video anonymizer that performs pixel-level modifications to anonymize each person's face, with minimal effect on action detection performance.
1 code implementation • CVPR 2018 • Zhongzheng Ren, Yong Jae Lee
In human learning, it is common to use multiple sources of information jointly.