Title: $\mathrm{D^3}$: Divide, Describe, and Diffuse: Prompt-Enriched, Scene-Aware Dataset Condensation for Object Detection
Primary area: datasets and benchmarks
Keywords: Dataset Condensation, Object detection

Abstract: Dataset condensation (DC) seeks to compress large datasets into small synthetic ones for efficient training. Recent work applies text-to-image diffusion models to DC, but their naïve use in object detection creates a representation bottleneck: conditioning on short captions yields sparse, single-object scenes with limited spatial coverage, failing to capture the multi-object co-occurrence and layout diversity essential for detector generalization.
We present $\mathrm{D^3}$ (Divide, Describe, Diffuse), an optimization-free framework that produces dense and semantically diverse synthetic images tailored for detection. $\mathrm{D^3}$ constructs a scene-to-object dictionary from dataset statistics, which guides a Large Language Model to generate enriched captions grounded in realistic contexts. To further enhance fidelity and spatial variety, we design two structured prompting strategies: prompt merging, which combines multiple enriched captions, and spatial partitioning, which allocates sub-prompts to different image regions.
On MS COCO and PASCAL VOC, $\mathrm{D^3}$ achieves state-of-the-art performance under severe data constraints. Notably, using only 0.5\% of VOC data, $\mathrm{D^3}$ reaches 18.8\% mAP (vs. 8.5\% for the strongest baseline), demonstrating its effectiveness in overcoming the representation gap of prior DC methods.