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Evidence record 25 · automatically gathered

SynCLIP: Synonym-Coherent Language-Image Pretraining for Robust Open-Vocabulary Dense Perception

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 SynC

Record details

Published: 13 July 2026
Source: arXiv
Category: Research
Topics: Bias & fairness
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness

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Shared topic: Bias & fairness

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Shared topic: Bias & fairness

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Shared topic: Bias & fairness

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Shared topic: Bias & fairness

Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias

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Shared topic: Bias & fairness

How Data Narratives Go Wrong: A Taxonomy of Issues Across the Data Communication Process

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How to cite this record

ethics.ai (13 July 2026), “SynCLIP: Synonym-Coherent Language-Image Pretraining for Robust Open-Vocabulary Dense Perception,” evidence record 25, https://ethics.ai/record/25 (originally published by arXiv).

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