activity
20182023
most citedEvaluating Bayesian Deep Learning Methods for Semantic Segmentation

147 citations · 297 across the 10 of their papers we have counts for

collaborators
Showing cs.CVShow all

5 papers · 1 filter

cs.CV2022

Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning

Jishnu Mukhoti, Tsung-Yu Lin, Omid Poursaeed +4

We introduce Patch Aligned Contrastive Learning (PACL), a modified compatibility function for CLIP's contrastive loss, intending to train an alignment between the patch tokens of t…

cs.CV2022★ 1 cited

Raising the Bar on the Evaluation of Out-of-Distribution Detection

Jishnu Mukhoti, Tsung-Yu Lin, Bor-Chun Chen +4

In image classification, a lot of development has happened in detecting out-of-distribution (OoD) data. However, most OoD detection methods are evaluated on a standard set of datas…

cs.CV2021★ 13 cited

Deep Deterministic Uncertainty for Semantic Segmentation

Jishnu Mukhoti, Joost van Amersfoort, Philip H. S. Torr +1

We extend Deep Deterministic Uncertainty (DDU), a method for uncertainty estimation using feature space densities, to semantic segmentation. DDU enables quantifying and disentangli…

cs.CV2019★ 1 cited

Let's Take This Online: Adapting Scene Coordinate Regression Network Predictions for Online RGB-D Camera Relocalisation

Tommaso Cavallari, Luca Bertinetto, Jishnu Mukhoti +2

Many applications require a camera to be relocalised online, without expensive offline training on the target scene. Whilst both keyframe and sparse keypoint matching methods can b…

cs.CV2018★ 147 cited

Evaluating Bayesian Deep Learning Methods for Semantic Segmentation

Jishnu Mukhoti, Yarin Gal

Deep learning has been revolutionary for computer vision and semantic segmentation in particular, with Bayesian Deep Learning (BDL) used to obtain uncertainty maps from deep models…