Generalized Small Object Detection:A Point-Prompted Paradigm and Benchmark

• URL: http://arxiv.org/abs/2604.02773v1
• Date: Fri, 03 Apr 2026 06:32:18 GMT
• Title: Generalized Small Object Detection:A Point-Prompted Paradigm and Benchmark
• Abstract summary: Small object detection (SOD) remains challenging due to extremely limited pixels and ambiguous object boundaries.<n>These characteristics lead to challenging annotation, limited availability of large-scale high-quality datasets, and inherently weak semantic representations for small objects.<n>In this work, we first address the data limitation by introducing TinySet-9M, the first large-scale, multi-domain dataset for small object detection.
• Score: 54.91847070147244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Small object detection (SOD) remains challenging due to extremely limited pixels and ambiguous object boundaries. These characteristics lead to challenging annotation, limited availability of large-scale high-quality datasets, and inherently weak semantic representations for small objects. In this work, we first address the data limitation by introducing TinySet-9M, the first large-scale, multi-domain dataset for small object detection. Beyond filling the gap in large-scale datasets, we establish a benchmark to evaluate the effectiveness of existing label-efficient detection methods for small objects. Our evaluation reveals that weak visual cues further exacerbate the performance degradation of label-efficient methods in small object detection, highlighting a critical challenge in label-efficient SOD. Secondly, to tackle the limitation of insufficient semantic representation, we move beyond training-time feature enhancement and propose a new paradigm termed Point-Prompt Small Object Detection (P2SOD). This paradigm introduces sparse point prompts at inference time as an efficient information bridge for category-level localization, enabling semantic augmentation. Building upon the P2SOD paradigm and the large-scale TinySet-9M dataset, we further develop DEAL (DEtect Any smalL object), a scalable and transferable point-prompted detection framework that learns robust, prompt-conditioned representations from large-scale data. With only a single click at inference time, DEAL achieves a 31.4% relative improvement over fully supervised baselines under strict localization metrics (e.g., AP75) on TinySet-9M, while generalizing effectively to unseen categories and unseen datasets. Our project is available at https://zhuhaoraneis.github.io/TinySet-9M/.

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