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GUIDED++: Enhancing Discrimination with Conjunctive Verification for Fine-Grained Open-Vocabulary Object Detection
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DOI：10.1007/s11263-026-02986-x.png)
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Abstract
En 中文
Fine-grained open-vocabulary detection (FG-OVD) challenges models to detect objects described by complex, compositional language. The prevalent strategy of encoding descriptive phrases into a single, holistic vector creates significant semantic ambiguity, hindering true compositional understanding. This ambiguity manifests as two opposing failures: attribute over-representation, where a visually dominant attribute leads to mislocalization, and attribute under-representation, where the model fails to enforce a conjunctive match across all specified attributes, leading to false positives. In this paper, we propose GUIDED++, a framework that systematically resolves both issues through a dual strategy. First, we employ architectural decoupling to separate coarse-grained localization from fine-grained discrimination, effectively mitigating attribute over-representation. Second, we introduce a novel conjunctive multi-attribute verification mechanism to explicitly combat attribute under-representation. This mechanism decomposes a complex query into its atomic attribute conditions, computes a distinct similarity score for each, and uses a minimal aggregation function to enforce a strict logical ‘AND’, ensuring an object satisfies all required attributes. Extensive experiments show that GUIDED++ significantly outperforms existing methods and establishes a new state-of-the-art on challenging FG-OVD benchmarks, demonstrating a more robust approach to compositional visual reasoning.
Keywords:
Fine-grained open vocabulary object detection
Open vocabulary object detection
Vision language models
Conjunctive verification
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