Dual Decoupling Training for Semi-supervised Object Detection with Noise-Bypass Head
Authors: Shida Zheng, Chenshu Chen, Xiaowei Cai, Tingqun Ye, Wenming Tan3526-3534
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method DDT is benchmarked on two popular detection datasets. One is PASCAL VOC, which includes VOC07 and VOC12 datasets. ... On MS-COCO benchmark, our method also achieves about 1.0 m AP improvements averaging across protocols compared with the prior state-of-the-art. |
| Researcher Affiliation | Industry | Hikvision Research Institute {zhengshida, chenchenshu, caixiaowei6, yetingqun, tanwenming}@hikvision.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | No explicit statement about the availability of open-source code or a link to a code repository was found. |
| Open Datasets | Yes | Our method DDT is benchmarked on two popular detection datasets. One is PASCAL VOC, which includes VOC07 and VOC12 datasets. ... MS-COCO (Lin et al. 2014) |
| Dataset Splits | Yes | In VOC07, we treat the trainval set as labeled data (5,011 images) and evaluate performance on the test set. Data from VOC12 trainval(11,540 images) and the subset of MS-COCO with the same classes as VOC (about 95k images) are used as extra unlabeled data. For MS-COCO, we randomly sample 1%/2%/5%/10% data from MS-COCO train2017 as the labeled data with the rest data as the unlabeled data. ... Table 2: The m AP at Io U=0.5:0.95 on MS-COCO val2017. |
| Hardware Specification | Yes | We adopt 8 NVIDIA Tesla V100 GPUs for all experiments. |
| Software Dependencies | No | The paper mentions software components like Faster RCNN, ResNet50, PyTorch (implicitly for deep learning frameworks), and SGD, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The classification loss is cross entropy loss for the RPN and focal loss for the ROI heads. DDT introduces four hyper-parameters to decouple the clean and noisy data. We set τ c l = 0.4, τ c h = 0.6, τ b l = 0.6 and τ b h = 0.8 unless otherwise specified. The optimizer we use is SGD with a momentum of 0.9. The size of a mini-batch is 32 with 16 labeled and 16 unlabeled images. ... the learning rate keeps constant during semi-supervised training, with 0.04 for VOC and 0.02 for MS-COCO. EMA ratio is set as α = 1e 4. |