FashionMV introduces product-level multi-view CIR, a 127K-product dataset built via automated LMM pipeline, and a 0.8B ProCIR model that beats larger baselines on three fashion benchmarks.
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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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FashionMV: Product-Level Composed Image Retrieval with Multi-View Fashion Data
FashionMV introduces product-level multi-view CIR, a 127K-product dataset built via automated LMM pipeline, and a 0.8B ProCIR model that beats larger baselines on three fashion benchmarks.
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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.