# FlowDet: Unifying Object Detection and Generative Transport Flows

Source: [FlowDet: Unifying Object Detection and Generative Transport Flows](<https://www.sota2.com/research/paper/6944dc24423ea42265729c89>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-20T05:00:10.481Z

Authors: Enis Baty, C. P. Bridges, Simon Hadfield

Published: 2025-12-18T17:03:49.000Z

## Abstract

We present FlowDet, the first formulation of object detection using modern Conditional Flow Matching techniques. This work follows from DiffusionDet, which originally framed detection as a generative denoising problem in the bounding box space via diffusion. We revisit and generalise this formulation to a broader class of generative transport problems, while maintaining the ability to vary the number of boxes and inference steps without re-training. In contrast to the curved stochastic transport paths induced by diffusion, FlowDet learns simpler and straighter paths resulting in faster scaling of detection performance as the number of inference steps grows. We find that this reformulation enables us to outperform diffusion based detection systems (as well as non-generative baselines) across a wide range of experiments, including various precision/recall operating points using multiple feature backbones and datasets. In particular, when evaluating under recall-constrained settings, we can highlight the effects of the generative transport without over-compensating with large numbers of proposals. This provides gains of up to +3.6% AP and +4.2% AP$\_{rare}$ over DiffusionDet on the COCO and LVIS datasets, respectively.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Object Detection on LVIS v1.0 (val)](<https://www.sota2.com/research/sota/object-detection-on-lvis-v1-0-val.md>): Object Detection; LVIS v1.0 (val); APbbox: 28.7.

- [Object Detection on COCO 2017 (val)](<https://www.sota2.com/research/sota/object-detection-on-coco-2017-val.md>): Object Detection; COCO 2017 (val); AP: 46.1.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2512.16771>)

[PDF](<https://arxiv.org/pdf/2512.16771.pdf>)