Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Conflict-Alleviated Gradient Descent for Adaptive Object Detection
Authors: Wenxu Shi, Bochuan Zheng
IJCAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 17,112 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,532 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL, EMAIL |
| 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. |