# FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection

Source: [FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection](<https://www.sota2.com/research/paper/6a1e6389dbd7150506f85199>)

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

Data updated: 2026-06-02T05:00:51.557Z

Authors: Yao Wei, Andrea Cavallaro, Changjae Oh

Published: 2026-05-30T16:01:43.000Z

## Abstract

Open-vocabulary object detection (OVD) has achieved remarkable progress through large-scale vision-language pre-training. Existing methods, however, typically formulate OVD as a discriminative prediction problem, where decoder queries are either static or initialized from encoder features, thus limiting their diversity and flexibility. In this paper, we introduce a generative perspective by modeling decoder query generation as a continuous transport process in latent space. We propose FlowOVD, a text-conditioned query generation framework based on rectified flow that progressively transforms text-agnostic queries into text-guided queries. By introducing continuous latent query dynamics into a vision-language model (VLM) based detector, our method avoids heuristic discrete query construction and enables more expressive semantic alignment for open-vocabulary detection. Without requiring additional training data, FlowOVD achieves 49.5 AP on COCO and 31.5 AP on LVIS, outperforming GroundingDINO by +1.2 AP (+2.5 %) and +4.1 AP (+15.0 %), respectively. The larger gain on the challenging long-tailed LVIS benchmark further highlights the effectiveness of continuous query generation for open-vocabulary generalization.

## 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 (minival)](<https://www.sota2.com/research/sota/object-detection-on-lvis-minival.md>): Object Detection; LVIS (minival); AP: 31.5.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2606.00782>)

[PDF](<https://arxiv.org/pdf/2606.00782.pdf>)