Sagnik Majumder

I am a PhD student in Computer Science at UT Austin, advised by Prof. Kristen Grauman. Before this, I received my MS in Computer Science at UT. I am broadly interested in computer vision and machine learning. My current line of research is embodied audio-visual understanding of 3D scenes with applications in mobile robotics and AR/VR.

Previously, I have worked with Prof. Visvanathan Ramesh at Goethe University on continual and meta learning for image recognition tasks. I have also had the pleasure of collaborating with Prof. Christoph Malsburg at the Frankfurt Institute for Advanced Studies for investigating visual models that draw motivation from Neuroscience.

Earlier, I graduated from BITS Pilani .

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Affiliations


BITS Pilani 2014-2018	FIAS Summer 2017	Goethe University 2018-2019	UT Austin 2019-present

News

Mar 2023	Our paper Chat2Map: Efficient Scene Mapping from Multi-Ego Conversations got accepted at CVPR 23!
Feb 2023	Co-organzing the SoundSpaces Challenge at the CVPR 2023 Embodied AI Workshop.
Dec 2022	Starting as a visiting researcher at Meta AI Research.
Sept 2022	Our paper Few-Shot Audio-Visual Learning of Environment Acoustics got accepted at NeurIPS 22!
Sept 2022	Continuing as a student researcher at Meta Reality Labs Redmond this Fall.
July 2022	Our paper Active Audio-Visual Separation of Dynamic Sound Sources got accepted at ECCV 22!
June 2022	Invited talk at CVPR 22 Sight and Sound Workshop, "Active Audio-Visual Separation of Dynamic Sound Sources" (Slides).
June 2022	Joined Meta Reality Labs Redmond as a research scientist intern this summer.
April 2022	Invited talk at Meta AI Research, "Active Audio-Visual Separation of Dynamic Sound Sources" (Slides).
Feb 2022	Co-organzing the SoundSpaces Challenge at the CVPR 2022 Embodied AI Workshop.
Jan 2022	Ported my old webpage to this new one. Will try to regulary update this space from now on!

Publications

	Chat2Map: Efficient Scene Mapping from Multi-Ego Conversations Sagnik Majumder, Hao Jiang, Pierre Moulon, Ethan Henderson, Paul Calamia, Kristen Grauman, Vamsi Krishna Ithapu Equal contribution CVPR 2023* paper \| project
	Retrospectives on the Embodied AI Workshop Matt Deitke, Dhruv Batra, Yonatan Bisk, ..., Sagnik Majumder, ..., Luca Weihs, Jiajun Wu arXiv paper
	Few-Shot Audio-Visual Learning of Environment Acoustics Sagnik Majumder, Changan Chen, Ziad Al-Halah, Kristen Grauman Equal contribution NeurIPS 2022* paper \| project \| code and data
	Active Audio-Visual Separation of Dynamic Sound Sources Sagnik Majumder, Kristen Grauman ECCV 2022 paper \| project \| code and data
	Move2Hear: Active Audio-Visual Source Separation Sagnik Majumder, Ziad Al-Halah, Kristen Grauman ICCV 2021 paper \| project \| code and data
	Learning to Set Waypoints for Audio-Visual Navigation Changan Chen, Sagnik Majumder, Ziad Al-Halah, Ruohan Gao, Santhosh K. Ramakrishnan, Kristen Grauman ICLR 2021 paper \| project \| code
	Model Agnostic Answer Reranking System for Adversarial Question Answering Sagnik Majumder, Chinmoy Samant, Greg Durrett EACL SRW 2021 paper
	Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos, Visvanathan Ramesh CVPR 2021 paper \| code and data
	Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition Martin Mundt, Sagnik Majumder, Iuliia Pliushch, Visvanathan Ramesh arXiv 2019 paper \| code
	Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers? Martin Mundt, Iuliia Pliushch, Sagnik Majumder, Visvanathan Ramesh ICCV SDLCV Workshop 2019 paper \| code
	Rethinking Layer-wise Feature Amounts in Convolutional Neural Network Architectures Martin Mundt, Sagnik Majumder, Tobias Weis, Visvanathan Ramesh NeurIPS CRACT Workshop 2018 paper \| code
	Handwritten Digit Recognition by Elastic Matching Sagnik Majumder, Christoph von der Malsburg, Aashish Richhariya, Surekha Bhanot JCP 2018 paper \| code

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