SL-Ground: Sentence-Level Grounding for Fine-Grained Open-Vocabulary Object Detection

Key points

• 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.1
• Based on this observation, we propose SL-Ground, a sentence-level grounding reformulation for grounding-based open-vocabulary detection.1
• Instead of scoring queries against fragmented token evidence, SL-Ground encodes each category description as a complete sentence-level representation and directly scores object queries against these representations.1
• At the finest car-model granularity, SL-Ground raises AP over the token-level variant from 14.5 to 70.6 under matched training candidate sampling and to 86.0 with full-vocabulary training, while better preserving open-vocabulary capability and avoiding repeated prompt chunking.1

Sources (1)

• [1]SL-Ground: Sentence-Level Grounding for Fine-Grained Open-Vocabulary Object Detection
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 06:40 PM

  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.

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