Rethinking Object Detection: What If Visual Targets Diverge from Universal Taxonomies?

Existing object detection methods typically assume that detection targets are specified in advance by semantic categories or language descriptions. Real-world applications, however, may require targets whose visual boundaries depend on application requirements and cannot be fully specified initially. We introduce Progressive Visual Target Alignment, a new detection task in which targets preserve their semantic identity while being defined according to application scenarios and progressively calibrated through interaction. We construct AST-50K, the first large-scale benchmark for this task, comprising 52,577 images from 43 application scenarios and 117 application-specific targets. We further establish a training-free agentic baseline that iteratively calibrates target and orchestrates complementary frozen visual tools through multimodal interaction. Experiments demonstrate that existing detection paradigms, including OVD, vision-language, and image-generation-based methods, suffer from inherent limitations on AST-50K, whereas our framework delivers substantial improvements.