activity
20182022
most citedRepPoints V2: Verification Meets Regression for Object Detection

70 citations · 280 across the 23 of their papers we have counts for

collaborators
Showing 2021Show all

8 papers · 1 filter

cs.CV2021★ 14 cited

Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning

Hanzhe Hu, Fangyun Wei, Han Hu +3

Due to the limited and even imbalanced data, semi-supervised semantic segmentation tends to have poor performance on some certain categories, e.g., tailed categories in Cityscapes…

cs.CV2021

Deep Structured Instance Graph for Distilling Object Detectors

Yixin Chen, Pengguang Chen, Shu Liu +2

Effectively structuring deep knowledge plays a pivotal role in transfer from teacher to student, especially in semantic vision tasks. In this paper, we present a simple knowledge s…

cs.CV2021★ 1 cited

Conditional Temporal Variational AutoEncoder for Action Video Prediction

Xiaogang Xu, Yi Wang, Liwei Wang +2

To synthesize a realistic action sequence based on a single human image, it is crucial to model both motion patterns and diversity in the action video. This paper proposes an Actio…

cs.CV2021★ 2 cited

Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervision

Yanwei Li, Hengshuang Zhao, Xiaojuan Qi +6

In this paper, we present a conceptually simple, strong, and efficient framework for fully- and weakly-supervised panoptic segmentation, called Panoptic FCN. Our approach aims to r…

cs.CV2021★ 18 cited

Collaborative Visual Navigation

Haiyang Wang, Wenguan Wang, Xizhou Zhu +2

As a fundamental problem for Artificial Intelligence, multi-agent system (MAS) is making rapid progress, mainly driven by multi-agent reinforcement learning (MARL) techniques. Howe…

cs.CV2021★ 2 cited

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency

Xin Lai, Zhuotao Tian, Li Jiang +4

Semantic segmentation has made tremendous progress in recent years. However, satisfying performance highly depends on a large number of pixel-level annotations. Therefore, in this…