# A Light-Weight Framework for Open-Set Object Detection with Decoupled Feature Alignment in Joint Space Source: [A Light-Weight Framework for Open-Set Object Detection with Decoupled Feature Alignment in Joint Space]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:12.092Z Authors: Yonghao He, Hu Su, Haiyong Yu, Cong Yang, Wei Sui, Cong Wang, Song Liu Published: 2024-12-19T09:32:53.000Z ## Abstract Open-set object detection (OSOD) is highly desirable for robotic manipulation in unstructured environments. However, existing OSOD methods often fail to meet the requirements of robotic applications due to their high computational burden and complex deployment. To address this issue, this paper proposes a light-weight framework called Decoupled OSOD (DOSOD), which is a practical and highly efficient solution to support real-time OSOD tasks in robotic systems. Specifically, DOSOD builds upon the YOLO-World pipeline by integrating a vision-language model (VLM) with a detector. A Multilayer Perceptron (MLP) adaptor is developed to transform text embeddings extracted by the VLM into a joint space, within which the detector learns the region representations of class-agnostic proposals. Cross-modality features are directly aligned in the joint space, avoiding the complex feature interactions and thereby improving computational efficiency. DOSOD operates like a traditional closed-set detector during the testing phase, effectively bridging the gap between closed-set and open-set detection. Compared to the baseline YOLO-World, the proposed DOSOD significantly enhances real-time performance while maintaining comparable accuracy. The slight DOSOD-S model achieves a Fixed AP of $26.7\\%$, compared to $26.2\\%$ for YOLO-World-v1-S and $22.7\\%$ for YOLO-World-v2-S, using similar backbones on the LVIS minival dataset. Meanwhile, the FPS of DOSOD-S is $57.1\\%$ higher than YOLO-World-v1-S and $29.6\\%$ higher than YOLO-World-v2-S. Meanwhile, we demonstrate that the DOSOD model facilitates the deployment of edge devices. The codes and models are publicly available at https://github.com/D-Robotics-AI-Lab/DOSOD. ## 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. - [Camouflaged Object Detection on OVCOD-D 1.0 (union of base and novel classes)](): Camouflaged Object Detection; OVCOD-D 1.0 (union of base and novel classes); AP: 53.4. - [Camouflaged Object Detection on OVCOD-D (AP, AP50, AP75)](): Camouflaged Object Detection; OVCOD-D; AP: 53.6. ## Original sources and implementation [Original paper]() [PDF]()