1 citations · 4 across the 16 of their papers we have counts for
8 papers · 1 filter
GUESS: Generative Uncertainty Ensemble for Self Supervision
Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh
Self-supervised learning (SSL) frameworks consist of pretext task, and loss function aiming to learn useful general features from unlabeled data. The basic idea of most SSL baselin…
Rethinking Self-Supervised Learning Within the Framework of Partial Information Decomposition
Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh
Self Supervised learning (SSL) has demonstrated its effectiveness in feature learning from unlabeled data. Regarding this success, there have been some arguments on the role that m…
Direct Coloring for Self-Supervised Enhanced Feature Decoupling
Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh
The success of self-supervised learning (SSL) has been the focus of multiple recent theoretical and empirical studies, including the role of data augmentation (in feature decouplin…
FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report Generation
Trong Thang Pham, Ngoc-Vuong Ho, Nhat-Tan Bui +8
Developing an interpretable system for generating reports in chest X-ray (CXR) analysis is becoming increasingly crucial in Computer-aided Diagnosis (CAD) systems, enabling radiolo…
Improving Accuracy and Generalization for Efficient Visual Tracking
Ram Zaveri, Shivang Patel, Yu Gu +1
Efficient visual trackers overfit to their training distributions and lack generalization abilities, resulting in them performing well on their respective in-distribution (ID) test…
TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering
Al Zadid Sultan Bin Habib, Kesheng Wang, Mary-Anne Hartley +2
Effective analysis of tabular data still poses a significant problem in deep learning, mainly because features in tabular datasets are often heterogeneous and have different levels…
Efficient Classification of Histopathology Images
Mohammad Iqbal Nouyed, Mary-Anne Hartley, Gianfranco Doretto +1
This work addresses how to efficiently classify challenging histopathology images, such as gigapixel whole-slide images for cancer diagnostics with image-level annotation. We use i…
Few-shot adaptation for morphology-independent cell instance segmentation
Ram J. Zaveri, Voke Brume, Gianfranco Doretto
Microscopy data collections are becoming larger and more frequent. Accurate and precise quantitative analysis tools like cell instance segmentation are necessary to benefit from th…