Object Detection Based on Region Decomposition and Assembly
Authors: Seung-Hwan Bae8094-8101
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We integrate the R-DAD into several feature extractors, and prove the distinct performance improvement on PASCAL07/12 and MSCOCO18 compared to the recent convolutional detectors. |
| Researcher Affiliation | Academia | Seung-Hwan Bae Computer Vision Lab., Department of Computer Science and Engineering Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Korea shbae@inu.ac.kr |
| Pseudocode | No | The paper describes the method using equations and text, but does not provide a formal pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing code or links to a code repository. |
| Open Datasets | Yes | We train and evaluate our R-DAD on standard detection benchmark datasets: PASCAL VOC07/12 (Everingham et al. 2015) and MSCOCO18 (Lin et al. 2014) datasets. |
| Dataset Splits | Yes | We train both detectors with the VOC07trainval (VOC07, 5011 images) and VOC12trainval sets (VOC07++12, 11540 images). |
| Hardware Specification | Yes | We implement all the detectors using the Caffe on a PC with a single TITAN Xp GPU without parallel and distributed training. |
| Software Dependencies | No | The paper mentions using 'Caffe' but does not provide specific version numbers for Caffe or any other software dependencies. |
| Experiment Setup | Yes | We use a learning rate µ = 1e 3 for 50k iterations, and µ = 1e 4 for the next 20k iterations on VOC07 evaluation. For VOC12 evaluation, we train a detector with µ = 1e 3 for 70k iterations, and continue it for 50k iterations with µ = 1e 4. For MSCOCO evaluation, we use µ = 1e 4 and µ = 1e 5 for the first 700k and the next 500k iterations. |