activity
20182021
most citedCompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation

15 citations · 18 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV20213 cited

Learning to Track Instances without Video Annotations

Yang Fu, Sifei Liu, Umar Iqbal +3

Tracking segmentation masks of multiple instances has been intensively studied, but still faces two fundamental challenges: 1) the requirement of large-scale, frame-wise annotation…

cs.DB2020

Clique: Spatiotemporal Object Re-identification at the City Scale

Tiantu Xu, Kaiwen Shen, Yang Fu +2

Object re-identification (ReID) is a key application of city-scale cameras. While classic ReID tasks are often considered as image retrieval, we treat them as spatiotemporal querie…

cs.CV202015 cited

CompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation

Yang Fu, Linjie Yang, Ding Liu +2

Video instance segmentation is a complex task in which we need to detect, segment, and track each object for any given video. Previous approaches only utilize single-frame features…

cs.CV2018

STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification

Yang Fu, Xiaoyang Wang, Yunchao Wei +1

In this work, we propose a novel Spatial-Temporal Attention (STA) approach to tackle the large-scale person re-identification task in videos. Different from the most existing metho…

cs.CV2018

Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification

Yang Fu, Yunchao Wei, Guanshuo Wang +3

Domain adaptation in person re-identification (re-ID) has always been a challenging task. In this work, we explore how to harness the natural similar characteristics existing in th…

cs.CV2018

Horizontal Pyramid Matching for Person Re-identification

Yang Fu, Yunchao Wei, Yuqian Zhou +5

Despite the remarkable recent progress, person re-identification (Re-ID) approaches are still suffering from the failure cases where the discriminative body parts are missing. To m…