# State and Scene Enhanced Prototypes for Weakly Supervised Open-Vocabulary Object Detection Source: [State and Scene Enhanced Prototypes for Weakly Supervised Open-Vocabulary Object Detection]() Collection index: [llms.txt]() Data updated: 2025-11-26T05:05:12.736Z Authors: Jiaying Zhou, Qingchao Chen Published: 2025-11-22T10:25:19.000Z ## Abstract Open-Vocabulary Object Detection (OVOD) aims to generalize object recognition to novel categories, while Weakly Supervised OVOD (WS-OVOD) extends this by combining box-level annotations with image-level labels. Despite recent progress, two critical challenges persist in this setting. First, existing semantic prototypes, even when enriched by LLMs, are static and limited, failing to capture the rich intra-class visual variations induced by different object states (e.g., a cat's pose). Second, the standard pseudo-box generation introduces a semantic mismatch between visual region proposals (which contain context) and object-centric text embeddings. To tackle these issues, we introduce two complementary prototype enhancement strategies. To capture intra-class variations in appearance and state, we propose the State-Enhanced Semantic Prototypes (SESP), which generates state-aware textual descriptions (e.g., "a sleeping cat") to capture diverse object appearances, yielding more discriminative prototypes. Building on this, we further introduce Scene-Augmented Pseudo Prototypes (SAPP) to address the semantic mismatch. SAPP incorporates contextual semantics (e.g., "cat lying on sofa") and utilizes a soft alignment mechanism to promote contextually consistent visual-textual representations. By integrating SESP and SAPP, our method effectively enhances both the richness of semantic prototypes and the visual-textual alignment, achieving notable improvements. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()