Improving Long-Tail Detection via Layout-Instruction Conditioned Diffusion Synthesis
Xinyu Zhang, Xiaodi Wang, Yanpeng Sun, Lingfeng Yang, Jingdong Wang, Lingqiao Liu
Synthetic data from diffusion models can improve object detection, but long-tail performance is still limited by scarce rare classes and inflexible conditioning (e.g., text-only or layout-only inputs). In this paper, we propose Tail-SD, a layout-instruction conditioned stable diffusion framework that supports text, layouts, or their hybrid, enabling controllable synthesis from boxes, masks, and/or prompts to better cover the long-tail objects. To train a single model across heterogeneous conditions, we introduce a multi-task cross-attention module and multi-task embedding tokens, improving generation quality and diversity. Extensive experiments on COCO and LVIS demonstrate that diffusion synthesis with hybrid conditioning consistently benefits detection, e.g., on LVIS val, it achieves +4.89% / +5.68% rare Box/Mask AP, highlighting strong potential for addressing long-tail challenges.
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