activity
20172022
most citedRandom Erasing Data Augmentation

748 citations · 1.1k across the 10 of their papers we have counts for

collaborators

14 papers

cs.CV20222 cited

Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning

Xiaoyue Duan, Guoliang Kang, Runqi Wang +4

Robust Model-Agnostic Meta-Learning (MAML) is usually adopted to train a meta-model which may fast adapt to novel classes with only a few exemplars and meanwhile remain robust to a…

cs.LG202228 cited

VAQF: Fully Automatic Software-Hardware Co-Design Framework for Low-Bit Vision Transformer

Mengshu Sun, Haoyu Ma, Guoliang Kang +5

The transformer architectures with attention mechanisms have obtained success in Nature Language Processing (NLP), and Vision Transformers (ViTs) have recently extended the applica…

cs.CV202067 cited

Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation

Guoliang Kang, Yunchao Wei, Yi Yang +2

Domain adaptive semantic segmentation aims to train a model performing satisfactory pixel-level predictions on the target with only out-of-domain (source) annotations. The conventi…

cs.CV20204 cited

Training-free Monocular 3D Event Detection System for Traffic Surveillance

Lijun Yu, Peng Chen, Wenhe Liu +2

We focus on the problem of detecting traffic events in a surveillance scenario, including the detection of both vehicle actions and traffic collisions. Existing event detection sys…

cs.CV2019

Attract or Distract: Exploit the Margin of Open Set

Qianyu Feng, Guoliang Kang, Hehe Fan +1

Open set domain adaptation aims to diminish the domain shift across domains, with partially shared classes. There exist unknown target samples out of the knowledge of source domain…

cs.IR201951 cited

Multi-Interest Network with Dynamic Routing for Recommendation at Tmall

Chao Li, Zhiyuan Liu, Mengmeng Wu +7

Industrial recommender systems usually consist of the matching stage and the ranking stage, in order to handle the billion-scale of users and items. The matching stage retrieves ca…