Soumitri Chattopadhyay (soumitri2001)

soumitri2001

Geek Repo

Company:CS PhD @ UNC Chapel Hill

Location:Chapel Hill, NC, USA

Home Page:https://soumitri2001.github.io

Twitter:@soumitri2001

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Organizations
CMATER-JUCS

Soumitri Chattopadhyay's repositories

awesome-signature-verification

A curated, non-exhaustive list of papers in handwritten signature verification using ML/DL techniques.

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SURDS-SSL-OSV

[ICPR 2022] "SURDS: Self-Supervised Attention-guided Reconstruction and Dual Triplet Loss for Writer Independent Offline Signature Verification", Intl. Conf. on Pattern Recognition (ICPR), 2022

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Pneumonia-Detection-Local-Search-aided-SCA

Based on our paper "Pneumonia Detection from Lung X-ray Images using Local Search Aided Sine Cosine Algorithm based Deep Feature Selection Method" [International Journal of Intelligent Systems, Wiley]

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SWIS-Self-Supervised-Learning-for-Offline-Signature-Verification

[ICIP 2022, Oral] "SWIS: Self-Supervised Representation Learning For Writer Independent Offline Signature Verification", IEEE Intl. Conf. on Image Processing (ICIP), 2022

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Wrapper-Filter-Speech-Emotion-Recognition

Implementation of our paper "A Hybrid Deep Feature Selection Framework for Emotion Recognition from Human Speeches" [Multimedia Tools and Applications, Springer]

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DMTNet-CRCH

Official Implementation of our paper "Supervision meets Self-Supervision: A Deep Multitask Network for Colorectal Cancer Histopathological Analysis" [Best Paper Award at MISP 2022]

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SmallDataSSL

[ICIP 2023] Exploring Self-Supervised Learning for Low-Resource Medical Image Analysis, IEEE Intl. Conf. on Image Processing (ICIP), 2023 (accepted)

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Flower-Species-Classification

Classification of flower images into respective classes using Deep Learning and Evolutionary Optimization.

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Generative-DeepLearning-TensorFlow

Contains colab notebooks and files pertaining to the course "Generative Deep Learning with TensorFlow" on Coursera.

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MASS-SemiSLSeg-UMM-MedIA22

MASS: Modality-collaborative semi-supervised segmentation by exploiting cross-modal consistency from unpaired CT and MRI images

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Pytorch-Medical-Segmentation

This repository is an unoffical PyTorch implementation of Medical segmentation in 2D and 3D.

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ummkd

[TMI'20] Unpaired Multi-modal Segmentation via Knowledge Distillation

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al-folio

A beautiful, simple, clean, and responsive Jekyll theme for academics

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Awesome-Vision-Attentions

Summary of related papers on visual attention. Related code will be released based on Jittor gradually.

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boundary-loss

Official code for "Boundary loss for highly unbalanced segmentation", runner-up for best paper award at MIDL 2019. Extended version in MedIA, volume 67, January 2021.

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Labs-JU-UG3

Assignment files of Laboratory classes for UG-3

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TGANet

TGANet: Text-guided attention for improved polyp segmentation [Accepted at MICCAI 2022]

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IDEAL

[ICASSP 2023] "IDEAL: Improved DEnse LocAL Contrastive Learning for Semi-Supervised Medical Image Segmentation", IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2023

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SnapEnsemFS

Based on our paper "SnapEnsemFS: A Snapshot Ensembling-based Deep Feature Selection Model for Colorectal Cancer Histological Analysis" published in Scientific Reports, Nature (2023).

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AECR-Net

Contrastive Learning for Compact Single Image Dehazing, CVPR2021

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Awesome-Sketch-Based-Applications

:books: A collection of sketch based application papers.

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ClipPrompt

A PyTorch implementation of ClipPrompt based on CVPR 2023 paper "CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not"

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Labs-UG2-Sem2

Code files for labs of 4th semester at JU IT Department , namely OOS, Networks and SWE.

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PyContrast

PyTorch implementation of Contrastive Learning methods

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Restormer

A PyTorch implementation of Restormer based on CVPR 2022 paper "Restormer: Efficient Transformer for High-Resolution Image Restoration"

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Snake-Game-using-js

The vintage Snake game, built using JavaScript.

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Vid2Persona

This project breathes life into video characters by using AI to describe their personality and then chat with you as them.

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