49 citations · 105 across the 22 of their papers we have counts for
6 papers · 1 filter
Trust your neighbours: Penalty-based constraints for model calibration
Balamurali Murugesan, Sukesh Adiga, Bingyuan Liu +3
Ensuring reliable confidence scores from deep networks is of pivotal importance in critical decision-making systems, notably in the medical domain. While recent literature on calib…
Class Adaptive Network Calibration
Bingyuan Liu, Jérôme Rony, Adrian Galdran +2
Recent studies have revealed that, beyond conventional accuracy, calibration should also be considered for training modern deep neural networks. To address miscalibration during le…
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
Junde Wu, Huihui Fang, Fei Li +26
Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both two modalities of images have prominent biomarker…
The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration
Bingyuan Liu, Ismail Ben Ayed, Adrian Galdran +1
In spite of the dominant performances of deep neural networks, recent works have shown that they are poorly calibrated, resulting in over-confident predictions. Miscalibration can…
Do We Really Need Dice? The Hidden Region-Size Biases of Segmentation Losses
Bingyuan Liu, Jose Dolz, Adrian Galdran +2
Most segmentation losses are arguably variants of the Cross-Entropy (CE) or Dice losses. On the surface, these two categories of losses seem unrelated, and there is no clear consen…
Furnishing Your Room by What You See: An End-to-End Furniture Set Retrieval Framework with Rich Annotated Benchmark Dataset
Bingyuan Liu, Jiantao Zhang, Xiaoting Zhang +3
Understanding interior scenes has attracted enormous interest in computer vision community. However, few works focus on the understanding of furniture within the scenes and a large…