Conflict-Alleviated Gradient Descent for Adaptive Object Detection
Authors: Wenxu Shi, Bochuan Zheng
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further validate our theoretical analysis and methods on several DAOD tasks, including cross-camera, weather, scene, and synthetic-to-real-world adaptation. Extensive experiments on multiple DAOD benchmarks demonstrate the effectiveness and superiority of our CAGrad approach. |
| Researcher Affiliation | Academia | 1School of Microelectronics and Communication Engineering, Chongqing University, China 2School of Computer Science, China West Normal University, China wxshi@cqu.edu.cn, zhengbc@vip.163.com |
| Pseudocode | Yes | Algorithm 1 CAGrad+DAOD Optimization Algorithm |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | Our methodology is rigorously evaluated on a variety of datasets, including KITTI [Geiger et al., 2012], Cityscapes [Cordts et al., 2016b], Foggy Cityscapes [Sakaridis et al., 2018b], Sim10k [Johnson-Roberson et al., 2016], and BDD100k [Yu et al., 2020] |
| Dataset Splits | Yes | Cityscapes as the source dataset, consisting of 2,975 training images and 500 evaluation images. [...] The BDD100k subset, encompassing 36,728 training and 5,258 validation images, is rich in diverse daylight scenes, each annotated with bounding boxes. [...] Sim10k [...] containing 10,000 training images with 58,701 bounding box annotations. Cityscapes is employed as the target domain, specifically focusing on car instances for both training and evaluation. |
| Hardware Specification | Yes | All these experiments are conducted using RTX3090 NVIDIA GPUs. |
| Software Dependencies | No | The paper mentions optimizers (SGD, Adam) and pre-trained models (ResNet50, VGG-16) but does not provide specific version numbers for any software dependencies like PyTorch, TensorFlow, or specific library versions. |
| Experiment Setup | Yes | Aligning with the Faster RCNN series, we train the network using the SGD optimizer with a momentum of 0.9 and a weight decay of 5 10 4. The initial learning rate is set to 1 10 3 and is reduced to 1 10 4 after 5 epochs. A total of 15 epochs are conducted, with a batch size of 2 maintained throughout. In line with the Deformable DETR series, we utilize the Adam optimizer [Kingma and Ba, 2015] for training over 50 epochs. The learning rate is initialized at 2 10 4 and reduced by a factor of 0.1 after 40 epochs. A batch size of 4 is employed consistently in all experiments. |