Few-Shot Object Detection via Association and DIscrimination
Authors: Yuhang Cao, Jiaqi Wang, Ying Jin, Tong Wu, Kai Chen, Ziwei Liu, Dahua Lin
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on standard Pascal VOC and MS-COCO datasets demonstrate that FADI achieves new state-of-the-art performance, significantly improving the baseline in any shot/split by +18.7. |
| Researcher Affiliation | Collaboration | 1CUHK-Sense Time Joint Lab, The Chinese University of Hong Kong 2Shanghai AI Laboratory 3Sense Time Research 4S-Lab, Nanyang Technological University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | In the ethics review checklist, the authors state: "Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] We will release the code to ensure strict reproducibility upon the paper accepted." |
| Open Datasets | Yes | We conduct experiments on both PASCAL VOC (07 + 12) [7] and MS COCO [18] datasets. |
| Dataset Splits | Yes | In few-shot detection, the training set is composed of a base set DB = {x B i , y B i } with abundant data of classes CB, and a novel set DN = {x N i , y N i } with few-shot data of classes CN, where xi and yi indicate training samples and labels, respectively. The number of objects for each class in CN is K for K-shot detection. The model is expected to detect objects in the test set with classes in CB CN. ... We then construct a balanced training set with K shots per class. |
| Hardware Specification | Yes | In the ethics review checklist, the authors state: "Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix 2." |
| Software Dependencies | No | We implement our methods based on MMDetection [3]. Faster-RCNN [21] with Feature Pyramid Network [17] and Res Net-101 [12] are adopted as base model. While MMDetection is mentioned, specific version numbers for it or other software dependencies are not provided. |
| Experiment Setup | Yes | Detailed settings are described in the supplementary material. ... During training, we freeze all parameters except the second linear layer FC 2, which means g( ; WN asso) is a single fc layer. We then construct a balanced training set with K shots per class. |