activity
20162021
most citedProgressive Graph Learning for Open-Set Domain Adaptation

39 citations · 116 across the 9 of their papers we have counts for

collaborators

15 papers

cs.CV2021

Mitigating Generation Shifts for Generalized Zero-Shot Learning

Zhi Chen, Yadan Luo, Sen Wang +3

Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information (e.g., attributes) to recognize the seen and unseen samples, where unseen classes are not obser…

cs.IR202124 cited

Context-Aware Attention-Based Data Augmentation for POI Recommendation

Yang Li, Yadan Luo, Zheng Zhang +2

With the rapid growth of location-based social networks (LBSNs), Point-Of-Interest (POI) recommendation has been broadly studied in this decade. Recently, the next POI recommendati…

cs.CV2021

Enhanced Modality Transition for Image Captioning

Ziwei Wang, Yadan Luo, Zi Huang

Image captioning model is a cross-modality knowledge discovery task, which targets at automatically describing an image with an informative and coherent sentence. To generate the c…

cs.SI2020

Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph

Yadan Luo, Zi Huang, Hongxu Chen +2

Signed link prediction in social networks aims to reveal the underlying relationships (i.e. links) among users (i.e. nodes) given their existing positive and negative interactions…

cs.CV202038 cited

Adversarial Bipartite Graph Learning for Video Domain Adaptation

Yadan Luo, Zi Huang, Zijian Wang +2

Domain adaptation techniques, which focus on adapting models between distributionally different domains, are rarely explored in the video recognition area due to the significant sp…

cs.CV202039 cited

Progressive Graph Learning for Open-Set Domain Adaptation

Yadan Luo, Zijian Wang, Zi Huang +1

Domain shift is a fundamental problem in visual recognition which typically arises when the source and target data follow different distributions. The existing domain adaptation ap…