Object-Aware Domain Generalization for Object Detection
Authors: Wooju Lee, Dasol Hong, Hyungtae Lim, Hyun Myung
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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 {dnwn24, ds.hong, shapelim, hmyung}@kaist.ac.kr |
| 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. |