OVS-DINO structurally aligns DINO with SAM to revitalize attenuated boundary features, achieving SOTA gains of 2.1% average and 6.3% on Cityscapes in weakly-supervised open-vocabulary segmentation.
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Table representations must be permutation-invariant to preserve semantic structure, and a new header-aligned encoder moves toward this ideal while exposing fragility in existing LLM table embeddings.
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OVS-DINO: Open-Vocabulary Segmentation via Structure-Aligned SAM-DINO with Language Guidance
OVS-DINO structurally aligns DINO with SAM to revitalize attenuated boundary features, achieving SOTA gains of 2.1% average and 6.3% on Cityscapes in weakly-supervised open-vocabulary segmentation.
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Towards Platonic Representation for Table Reasoning: A Foundation for Permutation-Invariant Retrieval
Table representations must be permutation-invariant to preserve semantic structure, and a new header-aligned encoder moves toward this ideal while exposing fragility in existing LLM table embeddings.