most citedNon-Salient Region Object Mining for Weakly Supervised Semantic Segmentation

17 citations · 33 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2021

Densely Semantic Enhancement for Domain Adaptive Region-free Detectors

Bo Zhang, Tao Chen, Bin Wang +3

Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled dat…

cs.CV202117 cited

Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation

Yazhou Yao, Tao Chen, Guosen Xie +5

Semantic segmentation aims to classify every pixel of an input image. Considering the difficulty of acquiring dense labels, researchers have recently been resorting to weak labels…

cs.CV20212 cited

EADNet: Efficient Asymmetric Dilated Network for Semantic Segmentation

Qihang Yang, Tao Chen, Jiayuan Fan +3

Due to real-time image semantic segmentation needs on power constrained edge devices, there has been an increasing desire to design lightweight semantic segmentation neural network…

cs.CV20211 cited

Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation

Tao Chen, Guosen Xie, Yazhou Yao +4

One-shot semantic image segmentation aims to segment the object regions for the novel class with only one annotated image. Recent works adopt the episodic training strategy to mimi…

cs.CV202013 cited

PIDNet: An Efficient Network for Dynamic Pedestrian Intrusion Detection

Jingchen Sun, Jiming Chen, Tao Chen +2

Vision-based dynamic pedestrian intrusion detection (PID), judging whether pedestrians intrude an area-of-interest (AoI) by a moving camera, is an important task in mobile surveill…

cs.CV2020

Coarse-to-Fine Gaze Redirection with Numerical and Pictorial Guidance

Jingjing Chen, Jichao Zhang, Enver Sangineto +3

Gaze redirection aims at manipulating the gaze of a given face image with respect to a desired direction (i.e., a reference angle) and it can be applied to many real life scenarios…