Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
UN-DETR: Promoting Objectness Learning via Joint Supervision for Unknown Object Detection
Authors: HaoMiao Liu, Hao Xu, Chuhuai Yue, Bo Ma
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our UN-DETR is comprehensively evaluated on multiple UOD and known detection benchmarks, demonstrating its effectiveness and achieving state-of-the-art performance. Experiment Following the UOD Benchmark, we utilize COCO-OOD, COCO-Mixed (Liang et al. 2023), and VOC (Everingham et al. 2010) as test sets and employ m AP, U-AP, U-F1, U-PRE, and U-REC as evaluation metrics, as detailed in the Appendix. Tables 1 and 2 present the results of our method UN-DETR, alongside 8 classic or recent stateof-the-art methods, on the UOD Benchmark. To examine the contribution of each component in our method, we conduct adequate ablation experiments as presented in Table 3. |
| Researcher Affiliation | Academia | Haomiao Liu*, Hao Xu*, Chuhuai Yue*, Bo Ma , Beijing Institute of Technology EMAIL |
| Pseudocode | No | The paper describes methods and processes in narrative text and figures, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/ndwxhmzz/UN-DETR |
| Open Datasets | Yes | Experiment Following the UOD Benchmark, we utilize COCO-OOD, COCO-Mixed (Liang et al. 2023), and VOC (Everingham et al. 2010) as test sets and employ m AP, U-AP, U-F1, U-PRE, and U-REC as evaluation metrics |
| Dataset Splits | No | The paper uses established benchmark datasets and refers to "test sets" (COCO-OOD, COCO-Mixed, VOC) and "training set" (VOC training set for pretraining), implying standard splits. However, it does not provide explicit percentages, sample counts, or a detailed methodology for splitting beyond stating which datasets are used for training and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers (e.g., PyTorch 1.9, CUDA 11.1). It mentions using ResNet50 as a backbone, which is a model architecture, not a software dependency with a specific version. |
| Experiment Setup | Yes | The weight parameters α and β are empirically set to 0.6 and 0.4, respectively. In Eq. 4, C is set to 0.5 and τ is set to 0.6. ... λ1, λ2, and λ3 are the weights of the loss, set to 3, 2, and 5, respectively. |