Search Results for author: Binod Bhattarai

Found 45 papers, 15 papers with code

Investigation of Federated Learning Algorithms for Retinal Optical Coherence Tomography Image Classification with Statistical Heterogeneity

no code implementations15 Feb 2024 Sanskar Amgain, Prashant Shrestha, Sophia Bano, Ignacio del Valle Torres, Michael Cunniffe, Victor Hernandez, Phil Beales, Binod Bhattarai

Purpose: We apply federated learning to train an OCT image classifier simulating a realistic scenario with multiple clients and statistical heterogeneous data distribution where data in the clients lack samples of some categories entirely.

Federated Learning Image Classification

Medical Vision Language Pretraining: A survey

no code implementations11 Dec 2023 Prashant Shrestha, Sanskar Amgain, Bidur Khanal, Cristian A. Linte, Binod Bhattarai

Medical Vision Language Pretraining (VLP) has recently emerged as a promising solution to the scarcity of labeled data in the medical domain.

Self-Supervised Learning

TextAug: Test time Text Augmentation for Multimodal Person Re-identification

no code implementations4 Dec 2023 Mulham Fawakherji, Eduard Vazquez, Pasquale Giampa, Binod Bhattarai

In this study, we investigate the effectiveness of two computer vision data augmentation techniques: cutout and cutmix, for text augmentation in multi-modal person re-identification.

Multimodal Deep Learning Person Re-Identification +3

Federated Active Learning for Target Domain Generalisation

1 code implementation4 Dec 2023 Razvan Caramalau, Binod Bhattarai, Danail Stoyanov

In this paper, we introduce Active Learning framework in Federated Learning for Target Domain Generalisation, harnessing the strength from both learning paradigms.

Active Learning Federated Learning +1

Dimension Mixer: A Generalized Method for Structured Sparsity in Deep Neural Networks

no code implementations30 Nov 2023 Suman Sapkota, Binod Bhattarai

The recent success of multiple neural architectures like CNNs, Transformers, and MLP-Mixers motivated us to look for similarities and differences between them.

Importance Estimation with Random Gradient for Neural Network Pruning

no code implementations31 Oct 2023 Suman Sapkota, Binod Bhattarai

Global Neuron Importance Estimation is used to prune neural networks for efficiency reasons.

Network Pruning

Multimodal Federated Learning in Healthcare: a review

no code implementations14 Oct 2023 Jacob Thrasher, Alina Devkota, Prasiddha Siwakotai, Rohit Chivukula, Pranav Poudel, Chaunbo Hu, Binod Bhattarai, Prashnna Gyawali

Recent advancements in multimodal machine learning have empowered the development of accurate and robust AI systems in the medical domain, especially within centralized database systems.

Federated Learning

Improving Medical Image Classification in Noisy Labels Using Only Self-supervised Pretraining

1 code implementation8 Aug 2023 Bidur Khanal, Binod Bhattarai, Bishesh Khanal, Cristian A. Linte

In this work, we explore contrastive and pretext task-based self-supervised pretraining to initialize the weights of a deep learning classification model for two medical datasets with self-induced noisy labels -- NCT-CRC-HE-100K tissue histological images and COVID-QU-Ex chest X-ray images.

Learning with noisy labels Medical Image Classification +1

A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation

no code implementations14 Jun 2023 Ronast Subedi, Rebati Raman Gaire, Sharib Ali, Anh Nguyen, Danail Stoyanov, Binod Bhattarai

This paper presents a solution to the cross-domain adaptation problem for 2D surgical image segmentation, explicitly considering the privacy protection of distributed datasets belonging to different centers.

Domain Adaptation Federated Learning +3

T2FNorm: Extremely Simple Scaled Train-time Feature Normalization for OOD Detection

no code implementations28 May 2023 Sudarshan Regmi, Bibek Panthi, Sakar Dotel, Prashnna K. Gyawali, Danail Stoyanov, Binod Bhattarai

Indeed, the naive incorporation of feature normalization within neural networks does not guarantee substantial improvement in OOD detection performance.

Out of Distribution (OOD) Detection

iEdit: Localised Text-guided Image Editing with Weak Supervision

no code implementations10 May 2023 Rumeysa Bodur, Erhan Gundogdu, Binod Bhattarai, Tae-Kyun Kim, Michael Donoser, Loris Bazzani

We propose a novel learning method for text-guided image editing, namely \texttt{iEdit}, that generates images conditioned on a source image and a textual edit prompt.

Contrastive Learning Descriptive +1

Translating Simulation Images to X-ray Images via Multi-Scale Semantic Matching

no code implementations16 Apr 2023 Jingxuan Kang, Tudor Jianu, Baoru Huang, Binod Bhattarai, Ngan Le, Frans Coenen, Anh Nguyen

In this paper, we propose a new method to translate simulation images from an endovascular simulator to X-ray images.

Image-to-Image Translation

Why is the winner the best?

no code implementations CVPR 2023 Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David G. Ellis, Sandy Engelhardt, Melanie Ganz, Noha Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, Ali Emre Kavur, Oldřich Kodym, Michal Kozubek, Jianning Li, Hongwei Li, Jun Ma, Carlos Martín-Isla, Bjoern Menze, Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patiño, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin Van Wijnen, Martin Wagner, Donglai Wei, Amine Yamlahi, Moi Hoon Yap, Chun Yuan, Maximilian Zenk, Aneeq Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, Jihoon Cho, Chanyeol Choi, Qi Dou, Ivan Ezhov, Christoph M. Friedrich, Clifton Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García Faura, Maria Grammatikopoulou, SeulGi Hong, Mostafa Jahanifar, Ikbeom Jang, Abdolrahim Kadkhodamohammadi, Inha Kang, Florian Kofler, Satoshi Kondo, Hugo Kuijf, Mingxing Li, Minh Huan Luu, Tomaž Martinčič, Pedro Morais, Mohamed A. Naser, Bruno Oliveira, David Owen, Subeen Pang, Jinah Park, Sung-Hong Park, Szymon Płotka, Elodie Puybareau, Nasir Rajpoot, Kanghyun Ryu, Numan Saeed, Adam Shephard, Pengcheng Shi, Dejan Štepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Helene Urien, João L. Vilaça, Kareem Abdul Wahid, Haojie Wang, Jiacheng Wang, Liansheng Wang, Xiyue Wang, Benedikt Wiestler, Marek Wodzinski, Fangfang Xia, Juanying Xie, Zhiwei Xiong, Sen yang, Yanwu Yang, Zixuan Zhao, Klaus Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein

The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.

Benchmarking Multi-Task Learning

Generalized Product-of-Experts for Learning Multimodal Representations in Noisy Environments

no code implementations7 Nov 2022 Abhinav Joshi, Naman Gupta, Jinang Shah, Binod Bhattarai, Ashutosh Modi, Danail Stoyanov

In order to process the multimodal information automatically and use it for an end application, Multimodal Representation Learning (MRL) has emerged as an active area of research in recent times.

3D Hand Pose Estimation Representation Learning +2

Noisy Heuristics NAS: A Network Morphism based Neural Architecture Search using Heuristics

1 code implementation10 Jul 2022 Suman Sapkota, Binod Bhattarai

Network Morphism based Neural Architecture Search (NAS) is one of the most efficient methods, however, knowing where and when to add new neurons or remove dis-functional ones is generally left to black-box Reinforcement Learning models.

Neural Architecture Search

Bimodal Camera Pose Prediction for Endoscopy

1 code implementation11 Apr 2022 Anita Rau, Binod Bhattarai, Lourdes Agapito, Danail Stoyanov

Deducing the 3D structure of endoscopic scenes from images is exceedingly challenging.

Pose Estimation Pose Prediction +1

Task-Aware Active Learning for Endoscopic Image Analysis

1 code implementation7 Apr 2022 Shrawan Kumar Thapa, Pranav Poudel, Binod Bhattarai, Danail Stoyanov

Semantic segmentation of polyps and depth estimation are two important research problems in endoscopic image analysis.

Active Learning Depth Estimation +2

A Unified Architecture of Semantic Segmentation and Hierarchical Generative Adversarial Networks for Expression Manipulation

no code implementations8 Dec 2021 Rumeysa Bodur, Binod Bhattarai, Tae-Kyun Kim

Recently, hierarchical networks that consist of both a global network dealing with the whole image and multiple local networks focusing on local parts are showing success.

Facial Expression Translation Image Manipulation +2

SeCGAN: Parallel Conditional Generative Adversarial Networks for Face Editing via Semantic Consistency

no code implementations17 Nov 2021 Jiaze Sun, Binod Bhattarai, Zhixiang Chen, Tae-Kyun Kim

Whilst both branches are required during training, the RGB branch is our primary network and the semantic branch is not needed for inference.

Attribute

Input Invex Neural Network

1 code implementation16 Jun 2021 Suman Sapkota, Binod Bhattarai

In the experiments section, we use our methods for classification tasks using an ensemble of 1-vs-all models as well as using a single multiclass model on larger-scale datasets.

Image Classification Image Segmentation +1

Visual Transformer for Task-aware Active Learning

1 code implementation7 Jun 2021 Razvan Caramalau, Binod Bhattarai, Tae-Kyun Kim

In this paper, we present a novel pipeline for pool-based Active Learning.

Active Learning

Label Geometry Aware Discriminator for Conditional Generative Networks

no code implementations12 May 2021 Suman Sapkota, Bidur Khanal, Binod Bhattarai, Bishesh Khanal, Tae-Kyun Kim

Multi-domain image-to-image translation with conditional Generative Adversarial Networks (GANs) can generate highly photo realistic images with desired target classes, yet these synthetic images have not always been helpful to improve downstream supervised tasks such as image classification.

Data Augmentation Image Classification +1

Active Learning for Bayesian 3D Hand Pose Estimation

1 code implementation1 Oct 2020 Razvan Caramalau, Binod Bhattarai, Tae-Kyun Kim

We propose a Bayesian approximation to a deep learning architecture for 3D hand pose estimation.

3D Hand Pose Estimation Active Learning

3D Dense Geometry-Guided Facial Expression Synthesis by Adversarial Learning

no code implementations30 Sep 2020 Rumeysa Bodur, Binod Bhattarai, Tae-Kyun Kim

We utilise this dataset to minimise the novel depth consistency loss via adversarial learning (note we do not have ground truth depth maps for generated face images) and the depth categorical loss of synthetic data on the discriminator.

3D Reconstruction

Sequential Graph Convolutional Network for Active Learning

1 code implementation CVPR 2021 Razvan Caramalau, Binod Bhattarai, Tae-Kyun Kim

We flip the label of newly queried nodes from unlabelled to labelled, re-train the learner to optimise the downstream task and the graph to minimise its modified objective.

Active Learning Hand Pose Estimation +1

MatchGAN: A Self-Supervised Semi-Supervised Conditional Generative Adversarial Network

no code implementations11 Jun 2020 Jiaze Sun, Binod Bhattarai, Tae-Kyun Kim

We perform augmentation by randomly sampling sensible labels from the label space of the few labelled examples available and assigning them as target labels to the abundant unlabelled examples from the same distribution as that of the labelled ones.

Attribute Generative Adversarial Network +1

Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Images

no code implementations23 Mar 2020 Sharib Ali, Binod Bhattarai, Tae-Kyun Kim, Jens Rittscher

In this work, we propose to use a few-shot learning approach that requires less training data and can be used to predict label classes of test samples from an unseen dataset.

Few-Shot Learning Test

Inducing Optimal Attribute Representations for Conditional GANs

no code implementations ECCV 2020 Binod Bhattarai, Tae-Kyun Kim

Existing conditional GANs commonly encode target domain label information as hard-coded categorical vectors in the form of 0s and 1s.

Attribute Multi-Task Learning +1

Sampling Strategies for GAN Synthetic Data

no code implementations10 Sep 2019 Binod Bhattarai, Seungryul Baek, Rumeysa Bodur, Tae-Kyun Kim

Unlike previous studies of randomly augmenting the synthetic data with real data, we present our simple, effective and easy to implement synthetic data sampling methods to train deep CNNs more efficiently and accurately.

Attribute Reinforcement Learning (RL)

Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model

1 code implementation ECCV 2018 Baris Gecer, Binod Bhattarai, Josef Kittler, Tae-Kyun Kim

We propose a novel end-to-end semi-supervised adversarial framework to generate photorealistic face images of new identities with wide ranges of expressions, poses, and illuminations conditioned by a 3D morphable model.

Domain Adaptation Face Generation +3

Deep fusion of visual signatures for client-server facial analysis

no code implementations1 Nov 2016 Binod Bhattarai, Gaurav Sharma, Frederic Jurie

The challenge addressed in this paper is to design a common universal representation such that a single merged signature is transmitted to the server, whatever be the type and number of features computed by the client, ensuring nonetheless an optimal performance.

Test

CP-mtML: Coupled Projection multi-task Metric Learning for Large Scale Face Retrieval

no code implementations CVPR 2016 Binod Bhattarai, Gaurav Sharma, Frederic Jurie

The experiments clearly demonstrate the scalability and improved performance of the proposed method on the tasks of identity and age based face image retrieval compared to competitive existing methods, on the standard datasets and with the presence of a million distractor face images.

Face Image Retrieval Metric Learning +2

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