Search Results for author: Zhongzheng Ren

Found 10 papers, 5 papers with code

UFO²: A Unified Framework towards Omni-supervised Object Detection

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.

Object Detection

Total Variation Optimization Layers for Computer Vision

1 code implementation7 Apr 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.

Edge Detection Image Classification +2

Class-agnostic Reconstruction of Dynamic Objects from Videos

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.

Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning

no code implementations6 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

3D Spatial Recognition without Spatially Labeled 3D

no code implementations 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.

3D Object Detection Multiple Instance Learning +2

UFO$^2$: A Unified Framework towards Omni-supervised Object Detection

no code implementations21 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.

Object Detection

Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning

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.

Learning to Anonymize Faces for Privacy Preserving Action Detection

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.

Action Detection

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