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Tail-SD uses diffusion synthesis to improve rare object detection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Tail-SD, a novel framework using diffusion models to generate synthetic data for improving object detection, particularly for rare classes. This method allows for controllable synthesis using text, layouts, or a combination of both, addressing limitations of previous text-only or layout-only approaches. Experiments on COCO and LVIS datasets showed significant improvements in detecting rare objects, with a notable increase in AP for rare categories. AI

IMPACT This research offers a new method for generating synthetic data to improve object detection, particularly for rare classes, which could benefit AI systems relying on accurate object recognition.

RANK_REASON The cluster describes a research paper proposing a new method for object detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

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Tail-SD uses diffusion synthesis to improve rare object detection

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The cluster describes a research paper proposing a new method for object detection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Improving Long-Tail Detection via Layout-Instruction Conditioned Diffusion Synthesis

    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 dif…

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