281 citations · 2k across the 103 of their papers we have counts for
18 papers · 1 filter
Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction
Yubao Sun, Ying Yang, Qingshan Liu +1
Hyperspectral compressive imaging takes advantage of compressive sensing theory to achieve coded aperture snapshot measurement without temporal scanning, and the entire three-dimen…
Geometry-aware Instance-reweighted Adversarial Training
Jingfeng Zhang, Jianing Zhu, Gang Niu +3
In adversarial machine learning, there was a common belief that robustness and accuracy hurt each other. The belief was challenged by recent studies where we can maintain the robus…
My Health Sensor, my Classifier: Adapting a Trained Classifier to Unlabeled End-User Data
Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann +8
In this work, we present an approach for unsupervised domain adaptation (DA) with the constraint, that the labeled source data are not directly available, and instead only access t…
Using Under-trained Deep Ensembles to Learn Under Extreme Label Noise
Konstantinos Nikolaidis, Thomas Plagemann, Stein Kristiansen +2
Improper or erroneous labelling can pose a hindrance to reliable generalization for supervised learning. This can have negative consequences, especially for critical fields such as…
Learning Realistic Patterns from Unrealistic Stimuli: Generalization and Data Anonymization
Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann +8
Good training data is a prerequisite to develop useful ML applications. However, in many domains existing data sets cannot be shared due to privacy regulations (e.g., from medical…
Helping Users Tackle Algorithmic Threats on Social Media: A Multimedia Research Agenda
Christian von der Weth, Ashraf Abdul, Shaojing Fan +1
Participation on social media platforms has many benefits but also poses substantial threats. Users often face an unintended loss of privacy, are bombarded with mis-/disinformation…
-Reference Transfer Learning for Saliency Prediction
Yan Luo, Yongkang Wong, Mohan S. Kankanhalli +1
Benefiting from deep learning research and large-scale datasets, saliency prediction has achieved significant success in the past decade. However, it still remains challenging to p…
Gender and Emotion Recognition from Implicit User Behavior Signals
Maneesh Bilalpur, Seyed Mostafa Kia, Mohan Kankanhalli +1
This work explores the utility of implicit behavioral cues, namely, Electroencephalogram (EEG) signals and eye movements for gender recognition (GR) and emotion recognition (ER) fr…
Robust Federated Recommendation System
Chen Chen, Jingfeng Zhang, Anthony K. H. Tung +2
Federated recommendation systems can provide good performance without collecting users' private data, making them attractive. However, they are susceptible to low-cost poisoning at…
Hierarchically Fair Federated Learning
Jingfeng Zhang, Cheng Li, Antonio Robles-Kelly +1
When the federated learning is adopted among competitive agents with siloed datasets, agents are self-interested and participate only if they are fairly rewarded. To encourage the…
Locality-Sensitive Hashing Scheme based on Longest Circular Co-Substring
Yifan Lei, Qiang Huang, Mohan Kankanhalli +1
Locality-Sensitive Hashing (LSH) is one of the most popular methods for -Approximate Nearest Neighbor Search (-ANNS) in high-dimensional spaces. In this paper, we propose a n…
Adversarial Learning for Personalized Tag Recommendation
Erik Quintanilla, Yogesh Rawat, Andrey Sakryukin +2
We have recently seen great progress in image classification due to the success of deep convolutional neural networks and the availability of large-scale datasets. Most of the exis…
Evaluating Salient Object Detection in Natural Images with Multiple Objects having Multi-level Saliency
Gökhan Yildirim, Debashis Sen, Mohan Kankanhalli +1
Salient object detection is evaluated using binary ground truth with the labels being salient object class and background. In this paper, we corroborate based on three subjective e…
Weakly-Supervised Multi-Person Action Recognition in 360 Videos
Junnan Li, Jianquan Liu, Yongkang Wong +2
The recent development of commodity 360 cameras have enabled a single video to capture an entire scene, which endows promising potentials in surveillance scenarios. Howev…
GradMix: Multi-source Transfer across Domains and Tasks
Junnan Li, Ziwei Xu, Yongkang Wong +2
The computer vision community is witnessing an unprecedented rate of new tasks being proposed and addressed, thanks to the deep convolutional networks' capability to find complex m…
Solving Raven's Progressive Matrices with Neural Networks
Tao Zhuo, Mohan Kankanhalli
Raven's Progressive Matrices (RPM) have been widely used for Intelligence Quotient (IQ) test of humans. In this paper, we aim to solve RPM with neural networks in both supervised a…
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
Jingfeng Zhang, Xilie Xu, Bo Han +4
Adversarial training based on the minimax formulation is necessary for obtaining adversarial robustness of trained models. However, it is conservative or even pessimistic so that i…
Direction Concentration Learning: Enhancing Congruency in Machine Learning
Yan Luo, Yongkang Wong, Mohan S. Kankanhalli +1
One of the well-known challenges in computer vision tasks is the visual diversity of images, which could result in an agreement or disagreement between the learned knowledge and th…