Context-Transformer: Tackling Object Confusion for Few-Shot Detection
Authors: Ze Yang, Yali Wang, Xianyu Chen, Jianzhuang Liu, Yu Qiao12653-12660
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate Context-Transformer on the challenging settings of few-shot detection and incremental few-shot detection. The experimental results show that, our framework outperforms the recent state-of-the-art approaches. |
| Researcher Affiliation | Collaboration | 1Shen Zhen Key Lab of Computer Vision and Pattern Recognition, SIAT-Sense Time Joint Lab, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 2Huawei Noah s Ark Lab 3SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making source code publicly available or a link to a code repository. |
| Open Datasets | Yes | First, we set VOC07+12 as our target-domain task. ... Second, we choose a source-domain benchmark for pretraining. ... we remove 20 categories of COCO that are overlapped with VOC, and use the rest 60 categories of COCO as source-domain data. |
| Dataset Splits | Yes | First, we set VOC07+12 as our target-domain task. The few-shot training set consists of N images (per category) that are randomly sampled from the original train/val set. Unless stated otherwise, N is 5 in our experiments. |
| Hardware Specification | Yes | Finally, we implement our approach with Py Torch (Paszke et al. 2017), where all the experiments run on 4 Titan Xp GPUs. |
| Software Dependencies | No | The paper mentions "Py Torch (Paszke et al. 2017)" but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For fine-tuning in the target domain, we set the implementation details where the batch size is 64, the optimization is SGD with momentum 0.9, the initial learning rate is 4 10 3 (decreased by 10 after 3k and 3.5k iterations), the weight decay is 5 10 4, the total number of training iterations is 4k. |