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ARXIV:2603.08347 · FEW-SHOT LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.08347FEW-SHOT LEARNINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Improve few-shot classification and OOD detection by learning shared global prompts and class-specific local prompts with sparse optimal transport.
Opportunity summary
Pain Improve few-shot classification and OOD detection by learning shared global prompts and class-specific local prompts with sparse optimal transport.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Improve few-shot classification and OOD detection by learning shared global prompts and class-specific local prompts with sparse optimal transport. Recent works extend this paradigm by incorporating local image-text alignment to capture fine-grained visual cues,…
Few-shot adaptation of vision-language models (VLMs) like CLIP typically relies on learning textual prompts matched to global image embeddings. Recent works extend this paradigm by incorporating local image-text alignment to capture fine-grained visual cues,…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. We propose SOT-GLP, which introduces a shared sparse patch support and balanced optimal transport allocation to explicitly partition salient visual regions among class-specific local…
Few-Shot Learning moved forward this cycle; last verified April 2026. Public score 8.0/10.
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Improve few-shot classification and OOD detection by learning shared global prompts and class-specific local prompts with sparse optimal transport.
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10.48550/arXiv.2603.08347Improve few-shot classification and OOD detection by learning shared global prompts and class-specific local prompts with sparse optimal transport.
Abstract
Few-shot adaptation of vision-language models (VLMs) like CLIP typically relies on learning textual prompts matched to global image embeddings. Recent works extend this paradigm by incorporating local image-text alignment to capture fine-grained visual cues, yet these approaches often select local regions independently for each prompt, leading to redundant local feature usage and prompt overlap. We propose SOT-GLP, which introduces a shared sparse patch support and balanced optimal transport allocation to explicitly partition salient visual regions among class-specific local prompts while preserving global alignment. Our method learns shared global prompts and class-specific local prompts. The global branch maintains standard image-text matching for robust category-level alignment. The local branch constructs a class-conditioned sparse patch set using V-V attention and aligns it to multiple class-specific prompts via balanced entropic optimal transport, yielding a soft partition of patches that prevents prompt overlap and collapse. We evaluate our method on two complementary objectives: (i) few-shot classification accuracy on 11 standard benchmarks and (ii) out-of-distribution (OOD) detection. On the standard 11-dataset benchmark with 16-shot ViT-B/16, SOT-GLP achieves 85.1% average accuracy, outperforming prior prompt-learning methods. We identify a distinct accuracy-robustness trade-off in prompt learning: while learnable projections optimize in-distribution fit, they alter the foundational feature space. We demonstrate that a projection-free local alignment preserves the native geometry of the CLIP manifold, yielding state-of-the-art OOD detection performance (94.2% AUC) that surpasses fully adapted models. Implementation available at: https://github.com/Deniz2304988/SOT-GLP
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PROBLEM
Improve few-shot classification and OOD detection by learning shared global prompts and class-specific local prompts with sparse optimal transport. Recent works extend this paradigm by incorporating local image-text alignment to capture fine-grained visual cues, yet these approa...
METHOD
Few-shot adaptation of vision-language models (VLMs) like CLIP typically relies on learning textual prompts matched to global image embeddings. Recent works extend this paradigm by incorporating local image-text alignment to capture fine-grained visual cues, yet these approaches...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. We propose SOT-GLP, which introduces a shared sparse patch support and balanced optimal transport allocation to explicitly partition salient visual regions among class-specific local prompts while preserv...
WHY NOW
Few-Shot Learning moved forward this cycle; last verified April 2026. Public score 8.0/10.
On the standard 11-dataset benchmark with 16-shot ViT-B/16, SOT-GLP achieves 85.1% average accuracy, outperforming prior prompt-learning methods.
Directly stated in abstract with specific numeric results
partial
yielding state-of-the-art OOD detection performance (94.2% AUC) that surpasses fully adapted models.
Directly stated in abstract with specific numeric results
partial
We propose SOT-GLP, which introduces a shared sparse patch support and balanced optimal transport allocation to explicitly partition salient visual regions among class-specific local prompts while preserving global alignment.
Directly stated in abstract as core methodological contribution
partial
Our method learns shared global prompts and class-specific local prompts. The global branch maintains standard image-text matching for robust category-level alignment.
Directly stated in abstract describing the method's architecture
partial
The local branch constructs a class-conditioned sparse patch set using V-V attention and aligns it to multiple class-specific prompts via balanced entropic optimal transport, yielding a soft partition of patches that prevents prompt overlap and collapse.
Directly stated in abstract describing technical implementation
partial
We identify a distinct accuracy-robustness trade-off in prompt learning: while learnable projections optimize in-distribution fit, they alter the foundational feature space.
Directly stated in abstract as an identified phenomenon
partial
We demonstrate that a projection-free local alignment preserves the native geometry of the CLIP manifold, yielding state-of-the-art OOD detection performance
Directly stated in abstract as a technical advantage
partial
Recent works extend this paradigm by incorporating local image-text alignment to capture fine-grained visual cues, yet these approaches often select local regions independently for each prompt, leading to redundant local feature usage and prompt overlap.
Directly stated in abstract as limitation of prior work
partial
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Improve few-shot classification and OOD detection by learning shared global prompts and class-specific local prompts with sparse optimal transport.
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