开放文本航空检测：面向航空场景理解的统一框架
Open-Text Aerial Detection: A Unified Framework For Aerial Visual Grounding And Detection
AI总结：
OTA-Det提出一个统一框架，整合开放词汇航空检测与遥感视觉定位，通过任务重述和密集语义对齐策略实现细粒度语义理解和多目标检测，达到最佳性能并保持实时推理效率。
摘要：
开放词汇航空检测（OVAD）和遥感视觉定位（RSVG）已成为航空场景理解的两个关键范式。然而，单独操作时每个范式都存在固有局限：OVAD受限于粗粒度类别级语义，而RSVG在结构上限制于单目标定位。这些限制阻碍了现有方法同时支持丰富语义理解和多目标检测。为此，我们提出了OTA-Det，第一个统一框架，将两种范式整合到一个统一的架构中。具体而言，我们引入了一个任务重述策略，统一了任务目标和监督机制，使跨两种范式数据集的联合训练成为可能，通过密集监督信号。此外，我们提出了一种密集语义对齐策略，建立了从整体表达到个体属性的多粒度显式对应关系，实现了细粒度语义理解。为确保实时效率，OTA-Det基于RT-DETR架构，通过引入几个高效模块将其扩展为开放文本检测，从而在六个涵盖OVAD和RSVG任务的基准上实现了最先进的性能，同时保持实时推理速度为34 FPS。
英文摘要：
Open-Vocabulary Aerial Detection (OVAD) and Remote Sensing Visual Grounding (RSVG) have emerged as two key paradigms for aerial scene understanding. However, each paradigm suffers from inherent limitations when operating in isolation: OVAD is restricted to coarse category-level semantics, while RSVG is structurally limited to single-target localization. These limitations prevent existing methods from simultaneously supporting rich semantic understanding and multi-target detection. To address this, we propose OTA-Det, the first unified framework that bridges both paradigms into a cohesive architecture. Specifically, we introduce a task reformulation strategy that unifies task objectives and supervision mechanisms, enabling joint training across datasets from both paradigms with dense supervision signals. Furthermore, we propose a dense semantic alignment strategy that establishes explicit correspondence at multiple granularities, from holistic expressions to individual attributes, enabling fine-grained semantic understanding. To ensure real-time efficiency, OTA-Det builds upon the RT-DETR architecture, extending it from closed-set detection to open-text detection by introducing several high efficient modules, achieving state-of-the-art performance on six benchmarks spanning both OVAD and RSVG tasks while maintaining real-time inference at 34 FPS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
