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Furthermore, a Spatial Attention Refinement (SAR) module selectively strengthens the most semantically relevant spatial regio","title":"SynCLIP: Synonym-Coherent Language-Image Pretraining for Robust Open-Vocabulary Dense Perception","url":"https://arxiv.org/abs/2607.11008","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.11008v1 Announce Type: cross \nAbstract: Open-vocabulary dense perception (OVDP) aims to localize objects unseen during training by leveraging textual knowledge. Despite the remarkable progress of recent CLIP-based approaches, we identify a critical limitation: synonym-induced grounding inconsistency, where semantically equivalent expressions yield disparate spatial attention patterns. This inconsistency undermines the robustness and performance of existing methods in real-world OVDP applications. To address this issue, we propose SynCLIP, a Synonym-Coherent Language-Image Pretraining framework that enhances synonym-robust grounding for OVDP. 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