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..
Object-Aware Domain Generalization for Object Detection
Authors: Wooju Lee, Dasol Hong, Hyungtae Lim, Hyun Myung
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the robustness of our method against out-of-distribution. We also conduct ablation studies to verify the effectiveness of proposed modules. and Table 1 shows the performance of the state-of-the-art models on clean and corrupted domains. |
| Researcher Affiliation | Academia | Urban Robotics Lab, School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Republic of Korea EMAIL |
| Pseudocode | No | The paper describes its methods through text and figures but does not contain any structured pseudocode or explicitly labeled algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/Wooju Lee24/OA-DG. |
| Open Datasets | Yes | Cityscapes-C (Michaelis et al. 2019) is a test benchmark to evaluate object detection robustness to corrupted domains. and Diverse Weather Dataset (DWD) is an urban-scene detection benchmark to assess object detection robustness to various weather conditions. DWD collected data from BDD-100k (2020), Foggy Cityscapes (2018), and Adverse Weather (2020) datasets. |
| Dataset Splits | No | The paper mentions using Cityscapes-C and Diverse Weather Dataset (DWD) which are standard benchmarks, and specifies training on "daytime-sunny" for DWD. However, it does not explicitly provide the specific percentages or sample counts for training, validation, and test splits used in their experiments, nor does it explicitly mention a validation set beyond what might be inherent in the benchmark usage. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers (e.g., PyTorch 1.9, CUDA 11.1) needed to replicate the experiment. |
| Experiment Setup | Yes | Temperature scaling parameter τ for contrastive loss is set to 0.06. We set λ and γ to 10 and 0.001. and Temperature scaling hyperparameter τ is set to 0.07. We set λ and γ to 10 and 0.001, respectively, for Faster R-CNN. |