Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmen…

Weakly supervised semantic segmentation is receiving great attention due to its low human annotation cost. In this paper, we aim to tackle bounding box supervised semantic segmentation, i.e., train…

Online Attention Accumulation for Weakly Supervised Semantic Segmentation

Object attention maps generated by image classifiers are usually used as priors for weakly supervised semantic segmentation. However, attention maps usually locate the most discriminative object pa…

Cross-Modal Progressive Comprehension for Referring Segmentation

Given a natural language expression and an image/video, the goal of referring segmentation is to produce the pixel-level masks of the entities described by the subject of the expression. Previous a…

Collaborative Video Object Segmentation by Multi-Scale Foreground-Background …

This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation. Unlike previous practices that focus on exploring the embedding lea…

Self-Correction for Human Parsing

Labeling pixel-level masks for fine-grained semantic segmentation tasks, e.g., human parsing, remains a challenging task. The ambiguous boundary between different semantic parts and those categorie…

Contrastive Adaptation Network for Single- and Multi-Source Domain Adaptation

Unsupervised domain adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain. Previous methods minimize the domain discrepancy …

Proposal-Free Network for Instance-Level Object Segmentation

STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation

Learning to Segment Human by Watching YouTube

HCP: A Flexible CNN Framework for Multi-Label Image Classification