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 • 11 Apr 2024 • Jing Wen, Xiaoming Zhao, Zhongzheng Ren, Alexander G. Schwing, Shenlong Wang
We introduce GoMAvatar, a novel approach for real-time, memory-efficient, high-quality animatable human modeling.
no code implementations • 2 Dec 2023 • Pengsheng Guo, Hans Hao, Adam Caccavale, Zhongzheng Ren, Edward Zhang, Qi Shan, Aditya Sankar, Alexander G. Schwing, Alex Colburn, Fangchang Ma
Our analysis identifies the core of these challenges as the interaction among noise levels in the 2D diffusion process, the architecture of the diffusion network, and the 3D model representation.
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.