120 citations · 133 across the 5 of their papers we have counts for
7 papers
Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing
Kaicheng Li, Hongyu Yang, Binghui Chen +3
Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack t…
ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization
Zichen Yang, Jie Qin, Di Huang
Weakly-supervised temporal action localization (WTAL) in untrimmed videos has emerged as a practical but challenging task since only video-level labels are available. Existing appr…
Improving Object Detection with Selective Self-supervised Self-training
Yandong Li, Di Huang, Danfeng Qin +2
We study how to leverage Web images to augment human-curated object detection datasets. Our approach is two-pronged. On the one hand, we retrieve Web images by image-to-image searc…
Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels
Lu Jiang, Di Huang, Mason Liu +1
Performing controlled experiments on noisy data is essential in understanding deep learning across noise levels. Due to the lack of suitable datasets, previous research has only ex…
Graph Representation Ensemble Learning
Palash Goyal, Di Huang, Sujit Rokka Chhetri +3
Representation learning on graphs has been gaining attention due to its wide applicability in predicting missing links, and classifying and recommending nodes. Most embedding metho…
ArduCode: Predictive Framework for Automation Engineering
Arquimedes Canedo, Palash Goyal, Di Huang +2
Automation engineering is the task of integrating, via software, various sensors, actuators, and controls for automating a real-world process. Today, automation engineering is supp…
Benchmarks for Graph Embedding Evaluation
Palash Goyal, Di Huang, Ankita Goswami +3
Graph embedding is the task of representing nodes of a graph in a low-dimensional space and its applications for graph tasks have gained significant traction in academia and indust…