AQT: Adversarial Query Transformers for Domain Adaptive Object Detection
Authors: Wei-Jie Huang, Yu-Lin Lu, Shih-Yao Lin, Yusheng Xie, Yen-Yu Lin
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thorough experiments over several domain adaptive object detection benchmarks demonstrate that our approach performs favorably against the state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Wei-Jie Huang1 , Yu-Lin Lu1 , Shih-Yao Lin2 , Yusheng Xie3 and Yen-Yu Lin1,4 1National Yang Ming Chiao Tung University 2Sony Corporation of America 3Amazon 4Academia Sinica |
| Pseudocode | No | The paper describes the proposed method using descriptive text and mathematical equations, but it does not include a formal pseudocode block or algorithm. |
| Open Source Code | Yes | Source code is available at https: //github.com/weii41392/AQT. |
| Open Datasets | Yes | Cityscapes [Cordts et al., 2016] is an urban scene dataset containing 2,975 training images and 500 validation images. Foggy Cityscapes [Sakaridis et al., 2018] is synthesized from and shared annotations with Cityscapes. BDD100k [Yu et al., 2020] is a large-scale driving dataset with diverse scenarios. Sim10k [Johnson-Roberson et al., 2017] is a synthetic driving dataset containing 10,000 images. |
| Dataset Splits | Yes | Cityscapes [Cordts et al., 2016] is an urban scene dataset containing 2,975 training images and 500 validation images. |
| Hardware Specification | No | The paper mentions that experiments were conducted but does not provide any specific hardware details such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions using 'Deformable DETR [Zhu et al., 2021] as our object detector with a Res Net-50 backbone pre-trained on Image Net [Deng et al., 2009]' but does not provide specific version numbers for software libraries or dependencies (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | We inherit most hyperparameters and training settings from Zhu et al., including the detection loss Ldet and Xavier initialization [Glorot and Bengio, 2010]. In Cityscapes to Foggy Cityscapes, all λsp, λch, and λins are set to 10 1. In the other settings, following [Saito et al., 2019], we adopt local alignment on the backbone and weak alignment using the focal loss [Lin et al., 2017]. The λsp, λch and λins are set to 10 1, 10 5, and 10 4, respectively. The batch size is set to 8 in all experiments. |