B Bhattarai MultiModal Learning Lab (MMLL)

B Bhattarai MultiModal Learning Lab (MMLL) is a research group within NAAMII that focuses on theoretical and applied research in Machine learning (ML) where the researches process information from heterogeneous sources such as vision, text, and speech to make computers understand, interpret and reason. Our applications include but are not limited to computer vision, medical image analysis and low-resource language processing.

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A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation
Data Engineering in Medical Imaging. DEMI, 2023
Bibtex

@article{subedi2023client,
  title={A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation},
  author={Subedi, Ronast and Gaire, Rebati Raman and Ali, Sharib and Nguyen, Anh and Stoyanov, Danail and Bhattarai, Binod},
  journal={arXiv preprint arXiv:2306.08720},
  year={2023}
}

Neural Network Pruning for Real-time Polyp Segmentation
MIUA, 2023
Bibtex

@article{sapkota2023neural,
  title={Neural Network Pruning for Real-time Polyp Segmentation},
  author={Sapkota, Suman and Poudel, Pranav and Regmi, Sudarshan and Panthi, Bibek and Bhattarai, Binod},
  journal={arXiv preprint arXiv:2306.13203},
  year={2023}
}

Sudarshan Regmi, Bibek Panthi, Sakar Dotel, Prashnna K Gyawali, Danail Stoynov, Binod Bhattarai
T2FNorm: Extremely Simple Scaled Train-time Feature Normalization for OOD Detection
arXiv preprint arXiv:2305.17797, 2023
Bibtex

@article{regmi2023t2fnorm,
  title={T2FNorm: Extremely Simple Scaled Train-time Feature Normalization for OOD Detection},
  author={Regmi, Sudarshan and Panthi, Bibek and Dotel, Sakar and Gyawali, Prashnna K and Stoynov, Danail and Bhattarai, Binod},
  journal={arXiv preprint arXiv:2305.17797},
  year={2023}
}

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