Subhrajyoti Dasgupta
Learning with machines.

I am a Master's student at Mila and Université de Montréal. I'm primarily interested in audio-visual learning, visual scene understanding and computational photography.

During my graduate studies, I was fortunate to have worked with Ruohan Gao(Meta), Prof. Mohamed Elhoseiny(KAUST) and Prof. Dinesh Manocha(UMD). I am also grateful to have collaborated with Senthil Yogamani(Qualcomm), Prof. Ciarán Eising(U.Limerick) and other wonderful researchers at Qualcomm and Valeo AI.

Previously, I worked under the guidance of Prof. Ujjwal Bhattacharya at the CVPR Unit, Indian Statistical Institute, Kolkata. Earlier, I have also completed a short stint at Bhabha Atomic Research Center, Mumbai where I worked on Devanagari text recognition in limited-data settings. I graduated from Amity University with a First Class with Distinction Bachelor's degree in Computer Science and Engineering.

My prior research experience has been on audio-visual co-segmentation, audio-visual summarization and medical signal processing.

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[Jul '23]: AdVerb is accepted at ICCV 2023! Check here!
[Jul '23]: UnShadowNet is accepted in IEEE Access journal!
[Sep '22]: Joined Mila as a Master's student. The program is supervised by Prof. Yoshua Bengio.
[Nov '21]: Presented AudViSum at BMVC 2021! [Presentation]
[Oct '21]: AudViSum accepted at BMVC 2021!
[Sep '21]: Presented Listen to the Pixels at ICIP 2021! [Presentation]
[May '21]: Listen to the Pixels accepted at ICIP 2021!
[Jan '21]: Presented CardioGAN at ICPR 2020! [Presentation]

AdVerb leverages visual cues of the environment to estimate clean audio from reverberant audio. For instance, given a reverberant sound produced in a large hall, our model attempts to remove the reverb effect to predict the anechoic or clean audio.

Shadow removal is a hard task given the challenges associated with it, one of them being unavailabilty of paired labelled data. We propose a weakly supervised, illumination critic guided method using contrastive learning for efficiently removing shadows.

Generating representative and diverse audio-visual summaries by exploiting both the audio and visual modalities, unlike prior works. Also presented a new dataset on TVSum and OVP with audio and visual annotations.

Audio-visual co-segmentation and sound source separation using a novel multimodal fusion mechanism, also addressing partially occluded sound source separation and co-segmentation for multiple but similar sound sources.

Generating synthetic ECGs, for easy sharing without risk of privacy breach, using an Attention-based Generative Adversarial Network.

While there exists large literature to detect and recognise English text in natural scenes and documents, during the time of this study, regional languages were not very largely studied. In this project done at Bhabha Atomic Research Center, Mumbai, dealt with a huge shortage of data and the nuances in the Devanagari script. Learning strategies for constrained settings like few-shot learning, transfer learning were used to develop the project. The project was implemented using Keras and Python. A great deal of OpenCV, Matplotlib and other scientific tools were also used.

A humongous amount of data is produced by the LHC per day. This data needs to be processed and used efficiently for further research. This study was on how Machine Learning can be implemented for particle identification, particle track reconstruction, clustering of particles based on similarity, and identifying rare decays. A study on the proposed SHiP experiment, with the scope of Machine Learning in it, was also done.

I often like to go out with my camera to cover music festivals, capture people and moments. Check out my works on 500px. Besides, I like indulging in a wide variety of movies and music. I keep a huge collection of movies from Kubrick to Nolan, Bergman to Ray.