Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection
Authors: NA DONG, Yongqiang Zhang, Mingli Ding, Gim Hee Lee
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the PASCAL VOC and MS COCO datasets show that our proposed method significantly outperforms other state-of-the-art class-incremental object detection methods when there is no co-occurrence between the base and novel classes during training. |
| Researcher Affiliation | Academia | Na Dong1,2 Yongqiang Zhang2 Mingli Ding2 Gim Hee Lee1 1Department of Computer Science, National University of Singapore 2School of Instrument Science and Engineering, Harbin Institute of Technology {dongna1994, zhangyongqiang, dingml}@hit.edu.cn gimhee.lee@comp.nus.edu.sg |
| Pseudocode | No | The paper describes its approach in detail using prose and mathematical equations but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our source code is available at https://github.com/dongnana777/Bridging-Non-Co-occurrence. |
| Open Datasets | Yes | Following [27], we evaluate our proposed method for class-incremental object detection on the PASCAL VOC 2007 and MS COCO 2014 datasets. |
| Dataset Splits | Yes | PASCAL VOC 2007 consists of about 5K training and validation images and 5K test images over 20 object categories. Models are trained on the trainval set and tested on the test set. MS COCO 2014 contains objects from 80 different categories with 83K images in the training set and 41K images in the validation set. We train models on the training set and evaluate models on the first 5K images of the validation set. |
| Hardware Specification | Yes | The training is carried out on 1 RTX 2080Ti GPU, and the batch size is set to 1. |
| Software Dependencies | No | The paper mentions using "Res Net-50 with frozen batch normalization layers as the backbone network" and "stochastic gradient descent with Nesterov momentum", but does not specify version numbers for any software libraries or frameworks (e.g., PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | The initial learning rate is set to 1e-3 and subsequently reduced by 0.1 after every 5 epochs for the previous model and the current model. Each model is trained for 20 epochs for both PASCAL VOC and MS COCO datasets. The training is carried out on 1 RTX 2080Ti GPU, and the batch size is set to 1. |