Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity
Authors: Yan Liu, Zhijie Zhang, Li Niu, Junjie Chen, Liqing Zhang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three benchmark datasets demonstrate the effectiveness of our method over existing methods. |
| Researcher Affiliation | Academia | Yan Liu , Zhijie Zhang , Li Niu , Junjie Chen, Liqing Zhang Mo E Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University {loseover, zzj506506, ustcnewly, chen.bys}@sjtu.edu.cn zhang-lq@cs.sjtu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Iterative Training Strategy |
| Open Source Code | Yes | Codes are available at https://github.com/bcmi/Tra Ma S-Weak-Shot-Object-Detection. |
| Open Datasets | Yes | Following [45], we investigate COCO 2017 detection dataset [27] as our source dataset. Besides, we choose ILSVRC 2013 detection dataset [32] as another source dataset. We take Pascal VOC 2007 [10] as the target dataset |
| Dataset Splits | Yes | COCO 2017 is composed of an official train/validation split with 118287 and 5000 images with 80 categories covering the 20 categories in VOC. After that, we denote the resulting COCO 2017 as COCO-60 which consists of 21987 training images and 921 validation images. Both the training set and validation set are merged together as the source dataset. ... The 200-category dataset, including the 20 categories in VOC, is split into training (395909 images) and validation (20121 images) sets. ... Pascal VOC 2007 [10] as the target dataset, which is split into trainval (5011 images) and test (4952 images) sets. |
| Hardware Specification | Yes | All experiments are conducted on two 24GB TITAN RTX, with a batch size of 16 images. |
| Software Dependencies | No | The paper mentions using Faster RCNN, ResNet-50, and SGD optimizer, and cites PyTorch, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The learning rate is initialized to 8 × 10−3 and reduced to 8 × 10−4. The weight decay and momentum are set to 1 × 10−4 and 0.9, respectively. The random seed is set to 222. ... we conduct four iterations (T = 4) following [45]. The threshold used to obtain candidate bounding boxes is 0.05. The threshold used to obtain pseudo bounding boxes of novel categories is 0.8. ... we use five image scales {480, 576, 688, 864, 1200} for both training and testing unless otherwise specified. ... α and γ are hyper-parameters set as 0.1 and 1.0 via cross-validation, respectively. ... β is a hyper-parameter set as 0.1 via cross-validation. ... In our experiments, we set both K and M to 8, so B = K × M = 64. |