1 citations · 2 across the 7 of their papers we have counts for
7 papers
Robust Calibration of Large Vision-Language Adapters
Balamurali Murugesan, Julio Silva-Rodriguez, Ismail Ben Ayed +1
This paper addresses the critical issue of miscalibration in CLIP-based model adaptation, particularly in the challenging scenario of out-of-distribution (OOD) samples, which has b…
Do not trust what you trust: Miscalibration in Semi-supervised Learning
Shambhavi Mishra, Balamurali Murugesan, Ismail Ben Ayed +2
State-of-the-art semi-supervised learning (SSL) approaches rely on highly confident predictions to serve as pseudo-labels that guide the training on unlabeled samples. An inherent…
Class and Region-Adaptive Constraints for Network Calibration
Balamurali Murugesan, Julio Silva-Rodriguez, Ismail Ben Ayed +1
In this work, we present a novel approach to calibrate segmentation networks that considers the inherent challenges posed by different categories and object regions. In particular,…
Neighbor-Aware Calibration of Segmentation Networks with Penalty-Based Constraints
Balamurali Murugesan, Sukesh Adiga Vasudeva, Bingyuan Liu +3
Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly in real-world domains such as healthcar…
Prompting classes: Exploring the Power of Prompt Class Learning in Weakly Supervised Semantic Segmentation
Balamurali Murugesan, Rukhshanda Hussain, Rajarshi Bhattacharya +2
Recently, CLIP-based approaches have exhibited remarkable performance on generalization and few-shot learning tasks, fueled by the power of contrastive language-vision pre-training…
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…
Calibrating Segmentation Networks with Margin-based Label Smoothing
Balamurali Murugesan, Bingyuan Liu, Adrian Galdran +2
Despite the undeniable progress in visual recognition tasks fueled by deep neural networks, there exists recent evidence showing that these models are poorly calibrated, resulting…