Multi-Granularity Graph-Convolution-Based Method for Weakly Supervised Person Search
Authors: Haichun Tai, De Cheng, Jie Li, Nannan Wang, Xinbo Gao
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate the effectiveness of the proposed method, and show superior performances to state-of-the-art methods by a large margin on the CUHK-SYSU and PRW datasets. |
| Researcher Affiliation | Academia | 1Xidian University 2Chongqing University of Posts and Telecommunications |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'Our method is implemented on the open-source library mmdetection [Chen et al., 2019]' but does not provide any link or statement about making their own source code available. |
| Open Datasets | Yes | CUHK-SYSU[Xiao et al., 2017] is a large-scale public dataset for person search... PRW[Zheng et al., 2017] is recorded by six separate cameras placed around the university. |
| Dataset Splits | No | The paper specifies the sizes for the training and test sets for CUHK-SYSU and PRW datasets, but does not explicitly mention a separate validation set split or its details. |
| Hardware Specification | Yes | Moreover, we conduct all the experiments on a single NVIDIA Ge Force RTX 3090 GPU. |
| Software Dependencies | No | The paper states: 'Our method is implemented on the open-source library mmdetection [Chen et al., 2019], where the backbone is built using Res Net50, pre-trained on Image Net[He et al., 2016].' It mentions software/libraries by name but does not provide specific version numbers. |
| Experiment Setup | Yes | We adopt the SGD optimizer with the batch size set to 4, the epochs set to 26, the momentum set to 0.9, and the weight decay set to 0.0005. The initial learning rate is 0.003 and is reduced by a factor of 10 at 22th and 24th epochs. For hyperparameters, we set m = 0.1, τ1 = 0.05, ϵ = 0.125, δ = 0.3, τ2 = 0.01, β = 0.1, γ = 0.3, λ = 0.25 for all datasets, while k1 = 10 for PRW and k1 = 5 for CUHK-SYSU. |