activity
20192022
most citedDisentangling Semantic-to-visual Confusion for Zero-shot Learning

30 citations · 64 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CV20221 cited

Multi-Label Continual Learning using Augmented Graph Convolutional Network

Kaile Du, Fan Lyu, Linyan Li +5

Multi-Label Continual Learning (MLCL) builds a class-incremental framework in a sequential multi-label image recognition data stream. The critical challenges of MLCL are the constr…

cs.LG202215 cited

Exploring Example Influence in Continual Learning

Qing Sun, Fan Lyu, Fanhua Shang +2

Continual Learning (CL) sequentially learns new tasks like human beings, with the goal to achieve better Stability (S, remembering past tasks) and Plasticity (P, adapting to new ta…

cs.CV2022

AGCN: Augmented Graph Convolutional Network for Lifelong Multi-label Image Recognition

Kaile Du, Fan Lyu, Fuyuan Hu +4

The Lifelong Multi-Label (LML) image recognition builds an online class-incremental classifier in a sequential multi-label image recognition data stream. The key challenges of LML…

cs.CV2021

Each Attribute Matters: Contrastive Attention for Sentence-based Image Editing

Liuqing Zhao, Fan Lyu, Fuyuan Hu +3

Sentence-based Image Editing (SIE) aims to deploy natural language to edit an image. Offering potentials to reduce expensive manual editing, SIE has attracted much interest recentl…

cs.CV202130 cited

Disentangling Semantic-to-visual Confusion for Zero-shot Learning

Zihan Ye, Fuyuan Hu, Fan Lyu +2

Using generative models to synthesize visual features from semantic distribution is one of the most popular solutions to ZSL image classification in recent years. The triplet loss…

cs.CV20204 cited

Coarse to Fine: Multi-label Image Classification with Global/Local Attention

Fan Lyu, Fuyuan Hu, Victor S. Sheng +3

In our daily life, the scenes around us are always with multiple labels especially in a smart city, i.e., recognizing the information of city operation to response and control. Gre…