A3B2 introduces an adaptive asymmetric adapter with uncertainty-aware dampening to reduce branch bias in few-shot vision-language image classification and outperforms standard adapter and prompt methods.
Vision- language model fine-tuning via simple parameter-efficient modification.arXiv preprint arXiv:2409.16718
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CDGLT achieves SOTA on MET-Meme for multimodal metaphor identification by using SLERP-based concept drift and prompt-adapted LayerNorm tuning with reduced compute.
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A$_3$B$_2$: Adaptive Asymmetric Adapter for Alleviating Branch Bias in Vision-Language Image Classification with Few-Shot Learning
A3B2 introduces an adaptive asymmetric adapter with uncertainty-aware dampening to reduce branch bias in few-shot vision-language image classification and outperforms standard adapter and prompt methods.
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Concept Drift Guided LayerNorm Tuning for Efficient Multimodal Metaphor Identification
CDGLT achieves SOTA on MET-Meme for multimodal metaphor identification by using SLERP-based concept drift and prompt-adapted LayerNorm tuning with reduced compute.