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Evidence record 18020 · automatically gathered

GRASP: Granularity-Aware Region Alignment and Semantic Prototype Learning for Fine-Grained Cross-Modal Understanding in Drone Views

Fine-grained cross-modal understanding in drone views is essential for aerial vision-language navigation. However, the inherent wide field of view and overhead perspective of drone scenarios impose dual challenges on vision-language understanding. At the macro level, overwhelming background clutter in visual representations leads to Cross-Modal Focus Misalignment, where the model prioritizes global environmental similarities over specific object details. At the micro level, Visual Isomorphism cr

Record details

Published: 10 August 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Environment
Retrieved: 11 August 2026

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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.

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Shared topic: Safety & alignment · Shared topic: Environment

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How to cite this record

ethics.ai (10 August 2026), “GRASP: Granularity-Aware Region Alignment and Semantic Prototype Learning for Fine-Grained Cross-Modal Understanding in Drone Views,” evidence record 18020, https://ethics.ai/record/18020 (originally published by arXiv).

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