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

• URL: http://arxiv.org/abs/2503.16106v1
• Date: Thu, 20 Mar 2025 12:51:19 GMT
• Title: OSLoPrompt: Bridging Low-Supervision Challenges and Open-Set Domain Generalization in CLIP
• Authors: Mohamad Hassan N C, Divyam Gupta, Mainak Singha, Sai Bhargav Rongali, Ankit Jha, Muhammad Haris Khan, Biplab Banerjee,
• Abstract summary: Low-Shot Open-Set Domain Generalization (LSOSDG) is a novel paradigm unifying low-shot learning with open-set domain generalization (ODG)<n>We propose OSLOPROMPT, an advanced prompt-learning framework for CLIP with two core innovations.
• Score: 15.780915391081734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 open-set samples with fine-grained semantics related to training classes. To address these challenges, we propose OSLOPROMPT, an advanced prompt-learning framework for CLIP with two core innovations. First, to manage limited supervision across source domains and improve DG, we introduce a domain-agnostic prompt-learning mechanism that integrates adaptable domain-specific cues and visually guided semantic attributes through a novel cross-attention module, besides being supported by learnable domain- and class-generic visual prompts to enhance cross-modal adaptability. Second, to improve outlier rejection during inference, we classify unfamiliar samples as "unknown" and train specialized prompts with systematically synthesized pseudo-open samples that maintain fine-grained relationships to known classes, generated through a targeted query strategy with off-the-shelf foundation models. This strategy enhances feature learning, enabling our model to detect open samples with varied granularity more effectively. Extensive evaluations across five benchmarks demonstrate that OSLOPROMPT establishes a new state-of-the-art in LSOSDG, significantly outperforming existing methods.

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