74 citations · 171 across the 7 of their papers we have counts for
7 papers
Discriminative Consensus Mining with A Thousand Groups for More Accurate Co-Salient Object Detection
Peng Zheng
Co-Salient Object Detection (CoSOD) is a rapidly growing task, extended from Salient Object Detection (SOD) and Common Object Segmentation (Co-Segmentation). It is aimed at detecti…
Bilateral Reference for High-Resolution Dichotomous Image Segmentation
Peng Zheng, Dehong Gao, Deng-Ping Fan +4
We introduce a novel bilateral reference framework (BiRefNet) for high-resolution dichotomous image segmentation (DIS). It comprises two essential components: the localization modu…
Memory-aided Contrastive Consensus Learning for Co-salient Object Detection
Peng Zheng, Jie Qin, Shuo Wang +2
Co-Salient Object Detection (CoSOD) aims at detecting common salient objects within a group of relevant source images. Most of the latest works employ the attention mechanism for f…
GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector
Peng Zheng, Huazhu Fu, Deng-Ping Fan +5
In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in na…
Facial-Sketch Synthesis: A New Challenge
Deng-Ping Fan, Ziling Huang, Peng Zheng +3
This paper aims to conduct a comprehensive study on facial-sketch synthesis (FSS). However, due to the high costs of obtaining hand-drawn sketch datasets, there lacks a complete be…
MovieNet-PS: A Large-Scale Person Search Dataset in the Wild
Jie Qin, Peng Zheng, Yichao Yan +3
Person search aims to jointly localize and identify a query person from natural, uncropped images, which has been actively studied over the past few years. In this paper, we delve…
Efficient Person Search: An Anchor-Free Approach
Yichao Yan, Jinpeng Li, Jie Qin +2
Person search aims to simultaneously localize and identify a query person from realistic, uncropped images. To achieve this goal, state-of-the-art models typically add a re-id bran…