activity
20192023
most citedRethinking the Item Order in Session-based Recommendation with Graph Neural Networks

339 citations · 849 across the 27 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

cs.CV2020★ 19 cited

Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation

Taotao Jing, Bingrong Xu, Jingjing Li +1

Domain adaptation (DA) becomes an up-and-coming technique to address the insufficient or no annotation issue by exploiting external source knowledge. Existing DA algorithms mainly…

cs.CV2020★ 3 cited

Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches

Zhi Chen, Sen Wang, Jingjing Li +1

Zero-shot learning (ZSL) is commonly used to address the very pervasive problem of predicting unseen classes in fine-grained image classification and other tasks. One family of sol…

cs.CV2020★ 14 cited

Accurate RGB-D Salient Object Detection via Collaborative Learning

Wei Ji, Jingjing Li, Miao Zhang +2

Benefiting from the spatial cues embedded in depth images, recent progress on RGB-D saliency detection shows impressive ability on some challenge scenarios. However, there are stil…

cs.IR2020

Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval

Lei Zhu, Hui Cui, Zhiyong Cheng +2

Social network stores and disseminates a tremendous amount of user shared images. Deep hashing is an efficient indexing technique to support large-scale social image retrieval, due…

cs.IR2020★ 1 cited

Task-adaptive Asymmetric Deep Cross-modal Hashing

Fengling Li, Tong Wang, Lei Zhu +2

Supervised cross-modal hashing aims to embed the semantic correlations of heterogeneous modality data into the binary hash codes with discriminative semantic labels. Because of its…

cs.IR2020★ 2 cited

Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation

Yang Xu, Lei Zhu, Zhiyong Cheng +2

Hashing is an effective technique to address the large-scale recommendation problem, due to its high computation and storage efficiency on calculating the user preferences on items…