Search Results for author: Vighnesh Birodkar

Found 9 papers, 4 papers with code

Proper Reuse of Image Classification Features Improves Object Detection

1 code implementation1 Apr 2022 Cristina Vasconcelos, Vighnesh Birodkar, Vincent Dumoulin

A common practice in transfer learning is to initialize the downstream model weights by pre-training on a data-abundant upstream task.

Image Classification Object Detection +1

The iWildCam 2021 Competition Dataset

no code implementations7 May 2021 Sara Beery, Arushi Agarwal, Elijah Cole, Vighnesh Birodkar

The challenge is to classify species and count individual animals across sequences in the test cameras.

Object Detection

The surprising impact of mask-head architecture on novel class segmentation

3 code implementations ICCV 2021 Vighnesh Birodkar, Zhichao Lu, Siyang Li, Vivek Rathod, Jonathan Huang

Under this family, we study Mask R-CNN and discover that instead of its default strategy of training the mask-head with a combination of proposals and groundtruth boxes, training the mask-head with only groundtruth boxes dramatically improves its performance on novel classes.

Instance Segmentation Semantic Segmentation

Straight to the point: reinforcement learning for user guidance in ultrasound

no code implementations2 Mar 2019 Fausto Milletari, Vighnesh Birodkar, Michal Sofka

Point of care ultrasound (POCUS) consists in the use of ultrasound imaging in critical or emergency situations to support clinical decisions by healthcare professionals and first responders.


Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Need

no code implementations29 Jan 2019 Vighnesh Birodkar, Hossein Mobahi, Samy Bengio

Large datasets have been crucial to the success of deep learning models in the recent years, which keep performing better as they are trained with more labelled data.

General Classification Image Classification +1

Unsupervised Learning of Disentangled Representations from Video

1 code implementation NeurIPS 2017 Emily Denton, Vighnesh Birodkar

We present a new model DrNET that learns disentangled image representations from video.


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