SL-Ground: Sentence-Level Grounding for Fine-Grained Open-Vocabulary Object Detection
Grounding-based open-vocabulary detectors provide a widely used framework for text-conditioned localization, with a phrase-grounding formulation that aligns object queries to individual text tokens and recovers class scores through token-to-class aggregation. We observe that this token-level scoring design becomes a bottleneck for fine-grained open-vocabulary object detection, where the target category is defined by a complete compositional description rather than by isolated tokens. Based on this observation, we propose SL-Ground, a sentence-level grounding reformulation for grounding-based open-vocabulary detection. Instead of scoring queries against fragmented token evidence, SL-Ground...
| Authors | Yang-Syu Li, Cheng-Huan Yang, Chia-Yung Jui et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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