activity
20192022
most citedWIDER Face and Pedestrian Challenge 2018: Methods and Results

28 citations · 56 across the 4 of their papers we have counts for

collaborators

6 papers

cs.MM20221 cited

SeCo: Separating Unknown Musical Visual Sounds with Consistency Guidance

Xinchi Zhou, Dongzhan Zhou, Wanli Ouyang +3

Recent years have witnessed the success of deep learning on the visual sound separation task. However, existing works follow similar settings where the training and testing dataset…

cs.CV202117 cited

Delving into Localization Errors for Monocular 3D Object Detection

Xinzhu Ma, Yinmin Zhang, Dan Xu +4

Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving, while accurate 3D object detection from this kind of data is very challenging. I…

cs.CV2020

Performance Optimization for Federated Person Re-identification via Benchmark Analysis

Weiming Zhuang, Yonggang Wen, Xuesen Zhang +5

Federated learning is a privacy-preserving machine learning technique that learns a shared model across decentralized clients. It can alleviate privacy concerns of personal re-iden…

cs.CV2020

Cheaper Pre-training Lunch: An Efficient Paradigm for Object Detection

Dongzhan Zhou, Xinchi Zhou, Hongwen Zhang +2

In this paper, we propose a general and efficient pre-training paradigm, Montage pre-training, for object detection. Montage pre-training needs only the target detection dataset wh…

cs.CV202010 cited

EcoNAS: Finding Proxies for Economical Neural Architecture Search

Dongzhan Zhou, Xinchi Zhou, Wenwei Zhang +4

Neural Architecture Search (NAS) achieves significant progress in many computer vision tasks. While many methods have been proposed to improve the efficiency of NAS, the search pro…

cs.CV201928 cited

WIDER Face and Pedestrian Challenge 2018: Methods and Results

Chen Change Loy, Dahua Lin, Wanli Ouyang +49

This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian. The challenge focuses on the problem of precise localization of human faces and bodies, and accurat…