Mohamad Hassan N C — accepted AI-conference papers

2025

OSLoPrompt: Bridging Low-Supervision Challenges and Open-Set Domain Generalization in CLIP

Mohamad Hassan N C, Divyam Gupta, Mainak Singha, Sai Bhargav Rongali, Ankit Jha, Muhammad Haris Khan +1

CVPR 2025poster

We introduce Low-Shot Open-Set Domain Generalization (LSOSDG), a novel paradigm unifying low-shot learning with open-set domain generalization (ODG). While prompt-based methods using models like CLIP have advanced DG, they falter in low-data regimes (e.g., 1-shot) and lack precision in detecting ope…

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2024

Enhancing the Domain Robustness of Self-Supervised pre-Training with Synthetic Images

Mohamad Hassan N C, Avigyan Bhattacharya, Victor G. Turrisi da Costa, Biplab Banerjee, Elisa Ricci

ICASSP 2024accepted

We present a novel method for improving the adaptability of self-supervised (SSL) pre-trained models across different domains. Our approach uses synthetic images that are generated using an auxiliary diffusion model, namely InstructPix2Pix. More specifically, starting from a real image, we prompt th…

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2024

SPDG-Net: Semantics Preserving Domain Augmentation through Style Interpolation for Multi-Source Domain Generalization

Advait Kumar, Shirsha Bose, Mohamad Hassan N C, Biplab Banerjee

ICASSP 2024accepted

This paper focuses on domain generalization (DG), addressing the challenge of robust classifier learning from multiple source domains for generalizing to unseen ones. DG suffers from limited source domain diversity, which may hinder model generalization. Recent studies explore domain-augmentation st…

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