most citedClass Activation Map Generation by Representative Class Selection and Multi-Layer Feature Fusion

10 citations · 21 across the 6 of their papers we have counts for

collaborators

8 papers

cs.CV20212 cited

BA^2M: A Batch Aware Attention Module for Image Classification

Qishang Cheng, Hongliang Li, Qingbo Wu +1

The attention mechanisms have been employed in Convolutional Neural Network (CNN) to enhance the feature representation. However, existing attention mechanisms only concentrate on…

eess.IV20212 cited

Advanced Geometry Surface Coding for Dynamic Point Cloud Compression

Jian Xiong, Hao Gao, Miaohui Wang +3

In video-based dynamic point cloud compression (V-PCC), 3D point clouds are projected onto 2D images for compressing with the existing video codecs. However, the existing video cod…

cs.CV20193 cited

A New Local Transformation Module for Few-shot Segmentation

Yuwei Yang, Fanman Meng, Hongliang Li +3

Few-shot segmentation segments object regions of new classes with a few of manual annotations. Its key step is to establish the transformation module between support images (annota…

eess.IV20192 cited

Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Image

Qingbo Wu, Lei Wang, King N. Ngan +3

Images acquired by outdoor vision systems easily suffer poor visibility and annoying interference due to the rainy weather, which brings great challenge for accurately understandin…

cs.CV2019

Class Activation Map generation by Multiple Level Class Grouping and Orthogonal Constraint

Kaixu Huang, Fanman Meng, Hongliang Li +3

Class activation map (CAM) highlights regions of classes based on classification network, which is widely used in weakly supervised tasks. However, it faces the problem that the cl…

cs.CV2019

A New Few-shot Segmentation Network Based on Class Representation

Yuwei Yang, Fanman Meng, Hongliang Li +2

This paper studies few-shot segmentation, which is a task of predicting foreground mask of unseen classes by a few of annotations only, aided by a set of rich annotations already e…