Exploiting Position Information in Convolutional Kernels for Structural Re-parameterization
Authors: Tianxiang Hao, Hui Chen, Guiguang Ding
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on four vision tasks, ranging from image-level tasks, and object-level tasks to pixel-level tasks. Experimental results show that PBConv can consistently achieve superior performance compared with existing state-of-the-art methods, improving plenty of architectures on various datasets and tasks. |
| Researcher Affiliation | Academia | Tianxiang Hao1,2 , Hui Chen3 and Guiguang Ding1 1School of Software, Tsinghua University 2Hangzhou Zhuoxi Institute of Brain and Intelligence 3Beijing National Research Center for Information Science and Technology (BNRist) EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes a "fast heuristic search algorithm" but does not provide it in a structured pseudocode or algorithm block format. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We do evaluation on CIFAR [Krizhevsky et al., 2009] and Image Net [Deng et al., 2009] classification, Cityscapes [Cordts et al., 2016] segmentation, Go Pro [Nah et al., 2017] deblurring and COCO [Lin et al., 2014] detection. |
| Dataset Splits | Yes | We do evaluation on CIFAR [Krizhevsky et al., 2009] and Image Net [Deng et al., 2009] classification, Cityscapes [Cordts et al., 2016] segmentation, Go Pro [Nah et al., 2017] deblurring and COCO [Lin et al., 2014] detection. Models are trained with Image Net pre-trained backbone weights on Cityscapes. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU, CPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any particular software dependencies with version numbers used for the experiments. |
| Experiment Setup | No | We first build a baseline and then replace its conv-BN sequence with ACB/DBB/PBConv, and train all models with identical configurations for a fair comparison. |