527 citations · 2.3k across the 108 of their papers we have counts for
11 papers · 2 filters
CalCErt: Bin-wise Certification of Confidence Calibration in Medical Image Classification
Leo Fillioux, Stergios Christodoulidis, Maria Vakalopoulou +1
Deep neural networks remain vulnerable to adversarial perturbations, which can distort not only predictions but also confidence scores, undermining uncertainty calibration. While e…
Test-Time Generalized Category Discovery
Shambhavi Mishra, Omprakash Chakraborty, Julio Silva-Rodriguez +3
Test-Time Adaptation (TTA) and Generalized Category Discovery (GCD) are traditionally treated as disjoint problems: the former adapts models to domain shift assuming all test class…
Continual Test-Time Adaptation in Computer Vision: Methods, Benchmarks, and Future Directions
Sarthak Kumar Maharana, Shambhavi Mishra, Yunbei Zhang +6
Deep neural nets achieve remarkable performance when training and test data share the same distribution, but this assumption frequently breaks in real-world deployment, where data…
PRiSM: Prototype Regularization for Few-Shot VLMs
Ghassen Baklouti, Omprakash Chakraborty, Jose Dolz +1
Training-free few-shot adaptation methods have gained significant attention recently in the context of Vision-language Models (VLMs). Yet, current benchmarks rely on strong assumpt…
Quantile Adaptive Temperature Scaling for Confidence Calibration
Omprakash Chakraborty, Leo Fillioux, Ismail Ben Ayed +1
Deep neural networks often produce poorly calibrated confidence estimates, overstating their certainty even when predictions are incorrect. Temperature Scaling remains the most wid…
ORION: ORthonormal Text Encoding for Universal VLM AdaptatION
Omprakash Chakraborty, Jose Dolz, Ismail Ben Ayed
Vision language models (VLMs) have demonstrated remarkable generalization across diverse tasks, yet their performance remains constrained by the quality and geometry of the textual…
Information Maximization for Long-Tailed Semi-Supervised Domain Generalization
Leo Fillioux, Omprakash Chakraborty, Quentin Gopée +6
Semi-supervised domain generalization (SSDG) has recently emerged as an appealing alternative to tackle domain generalization when labeled data is scarce but unlabeled samples acro…
Locality-Attending Vision Transformer
Sina Hajimiri, Farzad Beizaee, Fereshteh Shakeri +3
Vision transformers have demonstrated remarkable success in classification by leveraging global self-attention to capture long-range dependencies. However, this same mechanism can…
OCTOPUS: Enhancing the Spatial-Awareness of Vision SSMs with Multi-Dimensional Scans and Traversal Selection
Kunal Mahatha, Ali Bahri, Pierre Marza +5
State space models (SSMs) have recently emerged as an alternative to transformers due to their unique ability of modeling global relationships in text with linear complexity. Howev…
SPARK: Stochastic Propagation via Affinity-guided Random walK for training-free unsupervised segmentation
Kunal Mahatha, Jose Dolz, Christian Desrosiers
We argue that existing training-free segmentation methods rely on an implicit and limiting assumption, that segmentation is a spectral graph partitioning problem over diffusion-der…
SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning
Leo Fillioux, Omprakash Chakraborty, Ismail Ben Ayed +4
With the increasing adoption of vision-language models (VLMs) in critical decision-making systems such as healthcare or autonomous driving, the calibration of their uncertainty est…