Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
Authors: Bin-Bin Gao, Xiaochen Chen, Zhongyi Huang, Congchong Nie, Jun Liu, Jinxiang Lai, GUANNAN JIANG, Xi Wang, Chengjie Wang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically evaluate the proposed method for FSOD/g FSOD and FSIS/g FSIS tasks and demonstrate its effectiveness by comparison with state-of-the-art methods. |
| Researcher Affiliation | Industry | Bin-Bin Gao1 Xiaochen Chen1 Zhongyi Huang1 Congchong Nie1 Jun Liu1 Jinxiang Lai1 Guannan Jiang2 Xi Wang2 Chengjie Wang1 1Tencent You Tu Lab 2CATL |
| Pseudocode | No | The paper describes its methodology through mathematical equations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1https://csgaobb.github.io/Projects/DCFS. Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See Section 4 and https://csgaobb.github.io/Projects/DCFS. |
| Open Datasets | Yes | We follow the previous works and evaluate our method on PASCAL VOC [4] and MS-COCO [21] datasets. |
| Dataset Splits | Yes | For a fair comparison, we use the same data splits given in [33, 28]. |
| Hardware Specification | Yes | The experiments are conducted with Detectron2 [36] on NVIDIA GPU V100 on CUDA 11.0. |
| Software Dependencies | Yes | The experiments are conducted with Detectron2 [36] on NVIDIA GPU V100 on CUDA 11.0. |
| Experiment Setup | Yes | The SGD is used to optimize our network end-to-end with a mini-batch size of 16, momentum 0.9, and weight decay 5e 5 on 8 GPUs. The learning rate is set to 0.02 during base training and 0.01 during few-shot fine-tuning. |