Proposes Adaptive Tail-Head Alignment (ATHA) that breaks alignment for low-similarity 'tail tokens' in CLIP to boost source-free cross-domain few-shot learning.
Recon- struction target matters in masked image modeling for cross- domain few-shot learning.arXiv preprint arXiv:2412.19101,
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Proposes dynamic token re-weighting during target-domain fine-tuning to mitigate exacerbated attention sink in source-free CDFSL, achieving SOTA on four benchmarks.
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Improving CLIP Adaptation by Breaking Tail Alignment for Source-Free Cross-Domain Few-Shot Learning
Proposes Adaptive Tail-Head Alignment (ATHA) that breaks alignment for low-similarity 'tail tokens' in CLIP to boost source-free cross-domain few-shot learning.
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Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning
Proposes dynamic token re-weighting during target-domain fine-tuning to mitigate exacerbated attention sink in source-free CDFSL, achieving SOTA on four benchmarks.