Gaurav Mittal

I am a Senior Researcher at Microsoft Cloud+AI where I work on the research and development of Computer Vision and Machine Learning related products.

I am currently pursuing multi-modal machine learning research as part of Content Moderator under Azure Cognitive Services. Previously, I have worked on AutoML as part of the Microsoft Custom Vision Service. I did my Master's in Computer Vision at Robotics Institute, Carnegie Mellon University where I worked with Prof. Kris Kitani on model compression and Hypernetworks. I have also worked at Amazon as a Software Development Engineer. I graduated with a B.Tech. in Computer Science and Engineering from Indian Insitute of Technology Ropar with the President of India Gold Medal. I have won the Microsoft Imagine Cup India and Indian National Academy of Engineering Innovate Student Project Award in 2016 for my project SpotGarbage.

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• July 2022 : Paper accepted at ECCV 2022 (Oral) (details coming soon).
• May 2022 : Outstanding Reviewer for CVPR 2022.
• May 2022 : Reviewer for ECCV 2022.
• Mar 2022 : GateHUB accepted at CVPR 2022.
• Feb 2022 : Patent granted, Patent No.: US 11,238,885 B2.
• Dec 2021 : Reviewer for CVPR 2022.

I'm interested in computer vision, multmodal machine learning, automated machine learning (AutoML), semi-/self-supervised learning and meta learning.

GateHUB introduces a novel gated cross-attention along with future-augmented history and background suppression objective to outperform all existing methods on online action detection task on all public benchmarks.

First Unsupervised Meta-learning algorithm for Video Few-Shot action recognition. It comprises a novel Action-Appearance Aligned Meta-adaptation (A3M) module that learns to focus on the action-oriented video features in relation to the appearance features via explicit few-shot episodic meta-learning over unsupervised hard-mined episodes.

A novel information-theoretic approach to introduce dependency among features of a deep convolutional neural network (CNN). It jointly improve the expressivity of all features extracted from different layers in a CNN using Additive Information and Multiplicative Information.

A novel data augmentation technique that uses gradient-ascent to generate extra training samples for tail classes in a long-tail class distribution to improve generalization performance of a image classifier for real-world datasets exhibiting long tail. BLT avoids dedicated generative networks for image generation, thereby significantly reducing training time and compute.

A task-aware method to warm-start Hyperparameter Optimization (HPO) methods by predicting the performance for a hyperparamter configuration via a learned task (dataset) representation.

To make talking head generation robust to such emotional and noise variations, we propose an explicit audio representation learning framework that disentangles audio sequences into various factors such as phonetic content, emotional tone, background noise and others. When conditioned on disentangled content representation, the generated mouth movement by our model is significantly more accurate than previous approaches (without disentangled learning) in the presence of noise and emotional variations.

Proposed a method to generate an image incrementally based on a sequence of scene graphs such that the image content generated in previous steps is preserved and the cumulative image is modified as per the newly provided scene information.

Proposed a network architecture that learns long-term and short-term context of the video data and uses attention to align the information with accompanying text to perform variable length semantic video generation on unseen caption combinations.

Combines a variational autoencoder (VAE) with recurrent attention mechanism to create a temporally dependent sequence of frames that are gradually formed over time.

Designed a fully convolutional network to detect and coarsely segment garbage regions in the image. Built an smartphone app, SpotGarbage, deploying the CNN to make on-the-device detections. Also introduced a new Garbage-In-Images (GINI) dataset.

Proposed a novel encode-decoder network for brain extraction from T1-weighted MR images. The model operates on full 3D volumes, simplifying pre- and post-processing operations, to efficiently provide a voxel-wise binary mask delineating the brain region.

• Reviewer at ICLR 2022, ECCV 2022, NeurIPS 2022, AAAI 2021
  
• Outstanding Reviewer at ICCV 2021, CVPR 2021, CVPR 2022
• Program Committee member of IEEE Workshop on Computer Vision for Microscopy Image Analysis (CVMI) held in conjunction with CVPR 2022, CVPR 2021, CVPR 2020
• Program Chair of Workshop on Neural Architecture Search for Computer Vision in the Wild (NASFW) held in conjunction with WACV 2020

Respectfully copied from Jon Barron's website.