55 citations · 119 across the 6 of their papers we have counts for
7 papers
Dual-State Slot Attention: Decoupling Appearance and Identity for Video Object-Centric Learning
Sieu Tran, Duc Nguyen, Hao Vo +2
Unsupervised video object-centric learning aims to decompose dynamic scenes into persistent, object-level representations without supervision. However, existing slot-based methods…
Unsupervised Anomaly Detection on Temporal Multiway Data
Duc Nguyen, Phuoc Nguyen, Kien Do +3
Temporal anomaly detection looks for irregularities over space-time. Unsupervised temporal models employed thus far typically work on sequences of feature vectors, and much less on…
Explicitly Modeled Attention Maps for Image Classification
Andong Tan, Duc Tam Nguyen, Maximilian Dax +2
Self-attention networks have shown remarkable progress in computer vision tasks such as image classification. The main benefit of the self-attention mechanism is the ability to cap…
SELF: Learning to Filter Noisy Labels with Self-Ensembling
Duc Tam Nguyen, Chaithanya Kumar Mummadi, Thi Phuong Nhung Ngo +3
Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained with noisy labels for a long enough time. To overcome this problem, we present a simple and eff…
DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision
Duc Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi +4
Deep neural network (DNN) based salient object detection in images based on high-quality labels is expensive. Alternative unsupervised approaches rely on careful selection of multi…
Robust Learning Under Label Noise With Iterative Noise-Filtering
Duc Tam Nguyen, Thi-Phuong-Nhung Ngo, Zhongyu Lou +3
We consider the problem of training a model under the presence of label noise. Current approaches identify samples with potentially incorrect labels and reduce their influence on t…
Anomaly Detection With Multiple-Hypotheses Predictions
Duc Tam Nguyen, Zhongyu Lou, Michael Klar +1
In one-class-learning tasks, only the normal case (foreground) can be modeled with data, whereas the variation of all possible anomalies is too erratic to be described by samples.…