Search Results for author: Jishnu Mukhoti

Found 10 papers, 4 papers with code

Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning

no code implementations9 Dec 2022 Jishnu Mukhoti, Tsung-Yu Lin, Omid Poursaeed, Rui Wang, Ashish Shah, Philip H. S. Torr, Ser-Nam Lim

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 the vision encoder and the CLS token of the text encoder.

Contrastive Learning Image Classification +3

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

no code implementations24 Sep 2022 Jishnu Mukhoti, Tsung-Yu Lin, Bor-Chun Chen, Ashish Shah, Philip H. S. Torr, Puneet K. Dokania, Ser-Nam Lim

In this paper, we define 2 categories of OoD data using the subtly different concepts of perceptual/visual and semantic similarity to in-distribution (iD) data.

OOD Detection Out-of-Distribution Detection +2

Deep Deterministic Uncertainty for Semantic Segmentation

no code implementations29 Oct 2021 Jishnu Mukhoti, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal

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

Semantic Segmentation

Deep Deterministic Uncertainty: A Simple Baseline

4 code implementations23 Feb 2021 Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal

Reliable uncertainty from deterministic single-forward pass models is sought after because conventional methods of uncertainty quantification are computationally expensive.

Active Learning

Calibrating Deep Neural Networks using Focal Loss

2 code implementations NeurIPS 2020 Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip H. S. Torr, Puneet K. Dokania

To facilitate the use of focal loss in practice, we also provide a principled approach to automatically select the hyperparameter involved in the loss function.

The Intriguing Effects of Focal Loss on the Calibration of Deep Neural Networks

no code implementations25 Sep 2019 Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr, Puneet Dokania

When combined with temperature scaling, focal loss, whilst preserving accuracy and yielding state-of-the-art calibrated models, also preserves the confidence of the model's correct predictions, which is extremely desirable for downstream tasks.

Evaluating Bayesian Deep Learning Methods for Semantic Segmentation

1 code implementation30 Nov 2018 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 when predicting semantic classes.

Anomaly Detection Autonomous Driving +1

On the Importance of Strong Baselines in Bayesian Deep Learning

1 code implementation23 Nov 2018 Jishnu Mukhoti, Pontus Stenetorp, Yarin Gal

Like all sub-fields of machine learning Bayesian Deep Learning is driven by empirical validation of its theoretical proposals.

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