most citedRethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps

22 citations · 39 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CV202012 cited

Inter-Image Communication for Weakly Supervised Localization

Xiaolin Zhang, Yunchao Wei, Yi Yang

Weakly supervised localization aims at finding target object regions using only image-level supervision. However, localization maps extracted from classification networks are often…

cs.CV20201 cited

SASO: Joint 3D Semantic-Instance Segmentation via Multi-scale Semantic Association and Salient Point Clustering Optimization

Jingang Tan, Lili Chen, Kangru Wang +3

We propose a novel 3D point cloud segmentation framework named SASO, which jointly performs semantic and instance segmentation tasks. For semantic segmentation task, inspired by th…

cs.CV202022 cited

Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps

Xiaolin Zhang, Yunchao Wei, Yi Yang +1

Recently, remarkable progress has been made in weakly supervised object localization (WSOL) to promote object localization maps. The common practice of evaluating these maps applie…

cs.CV2020

NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

Abdelrahman Abdelhamed, Mahmoud Afifi, Radu Timofte +87

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new versi…

cs.RO20204 cited

3DCFS: Fast and Robust Joint 3D Semantic-Instance Segmentation via Coupled Feature Selection

Liang Du, Jingang Tan, Xiangyang Xue +5

We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation. Insp…

cs.CV2018

SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation

Xiaolin Zhang, Yunchao Wei, Yi Yang +1

One-shot image semantic segmentation poses a challenging task of recognizing the object regions from unseen categories with only one annotated example as supervision. In this paper…