Differential Alignment for Domain Adaptive Object Detection
Authors: Xinyu He, Xinhui Li, Xiaojie Guo
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on widely-used DAOD datasets together with ablation studies are conducted to demonstrate the efficacy of our proposed method and reveal its superiority over other SOTA alternatives. |
| Researcher Affiliation | Academia | Xinyu He, Xinhui Li, Xiaojie Guo* College of Intelligence and Computing, Tianjin University, Tianjin, China xy EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using textual explanations and mathematical equations, but it does not include a clearly labeled pseudocode block or algorithm section. |
| Open Source Code | Yes | Code https://github.com/Estrella-Xyu/Differential-Alignment-for-DAOD |
| Open Datasets | Yes | Cityscapes (Cordts et al. 2016) comprises 2,975 training images and 500 validation images, covering various urban environments and traffic conditions. [...] Foggy Cityscapes (Sakaridis et al. 2018) is a synthetic dataset rendered from Cityscapes with three levels of foggy density (0.005, 0.01, 0.02). [...] Sim10k (Johnson-Roberson et al. 2016) contains 10,000 images rendered from GTA engine. [...] BDD100k-daytime (Yu et al. 2020) is a subset of the larger BDD100k dataset, specially designed for daytime scenarios. |
| Dataset Splits | Yes | Cityscapes (Cordts et al. 2016) comprises 2,975 training images and 500 validation images, covering various urban environments and traffic conditions. [...] BDD100k-daytime (Yu et al. 2020) [...] It contains 36,728 training images and 5,258 validation images |
| Hardware Specification | Yes | Our implementation is based on the Py Torch framework and the model is trained on 4 NVIDIA RTX3090 GPUs with 24 GB of memory each. |
| Software Dependencies | No | The paper mentions "Py Torch framework" but does not specify a version number for PyTorch or any other software dependencies with their versions. |
| Experiment Setup | Yes | We optimize the network using the SGD optimizer with a momentum of 0.9. The initial learning rate is set to 0.01 and decreases in the final iteration. We use a batch size of 32, consisting of 16 labeled source images and 16 unlabeled target ones, and train the network for 25k iterations in total, including 10,000 iterations for burn-in and 15,000 iterations for teacher-student mutual learning. [...] In our experiments, setting α to 0.9996 works sufficiently well. [...] In our experiments, we set λ as 0.01 by default. |