activity
20202022
most citedSelf-supervised Graph-level Representation Learning with Local and Global Structure

28 citations · 63 across the 5 of their papers we have counts for

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6 papers · 1 filter

cs.CV20223 cited

Learning Continuous Depth Representation via Geometric Spatial Aggregator

Xiaohang Wang, Xuanhong Chen, Bingbing Ni +2

Depth map super-resolution (DSR) has been a fundamental task for 3D computer vision. While arbitrary scale DSR is a more realistic setting in this scenario, previous approaches pre…

cs.CV2021

Cross-category Video Highlight Detection via Set-based Learning

Minghao Xu, Hang Wang, Bingbing Ni +3

Autonomous highlight detection is crucial for enhancing the efficiency of video browsing on social media platforms. To attain this goal in a data-driven way, one may often face the…

cs.CV202123 cited

X-volution: On the unification of convolution and self-attention

Xuanhong Chen, Hang Wang, Bingbing Ni

Convolution and self-attention are acting as two fundamental building blocks in deep neural networks, where the former extracts local image features in a linear way while the latte…

cs.CV2021

3D Human Action Representation Learning via Cross-View Consistency Pursuit

Linguo Li, Minsi Wang, Bingbing Ni +3

In this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D skeleton-based action Representation (CrosSCLR), by leveraging multi-view complementary sup…

cs.CV20209 cited

Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation

Hang Wang, Minghao Xu, Bingbing Ni +1

Transferring knowledges learned from multiple source domains to target domain is a more practical and challenging task than conventional single-source domain adaptation. Furthermor…

cs.CV2020

Cross-domain Detection via Graph-induced Prototype Alignment

Minghao Xu, Hang Wang, Bingbing Ni +2

Applying the knowledge of an object detector trained on a specific domain directly onto a new domain is risky, as the gap between two domains can severely degrade model's performan…