Region-Based Quality Estimation Network for Large-Scale Person Re-Identification
Authors: Guanglu Song, Biao Leng, Yu Liu, Congrui Hetang, Shaofan Cai
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the performance of RQEN on two publicly available video datasets and proposed large-scale dataset for video-based person re-id: the PRID 2011 dataset (Hirzer et al. 2011), i LIDS-VID dataset (Wang et al. 2014) ,MARS (Zheng et al. 2016)and LPW. |
| Researcher Affiliation | Academia | Guanglu Song,1 Biao Leng,1 Yu Liu,2 Congrui Hetang,1 Shaofan Cai1 1 School of Computer Science and Engineering, Beihang University, Beijing 100191, China 2 The Chinese University of Hong Kong, Hong Kong |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link to a dataset (http://liuyu.us/ dataset/lpw/index.html) but does not provide concrete access to the source code for the methodology described in the paper. |
| Open Datasets | Yes | We propose a new dataset named the Labeled Pedestrian in the Wild (LPW) . It contains 2,731 pedestrians in three different scenes where each annotated identity is captured by from 2 to 4 cameras. The LPW features a notable scale of 7,694 tracklets with over 590,000 images as well as the cleanliness of its tracklets. It distinguishes from existing datasets in three aspects: large scale with cleanliness, automatically detected bounding boxes and far more crowded scenes with greater age span. This dataset provides a more realistic and challenging benchmark, which facilitates the further exploration of more powerful algorithms. It can be available on http://liuyu.us/ dataset/lpw/index.html. |
| Dataset Splits | No | The paper describes training and test splits for datasets, but does not explicitly detail a separate validation set with specific percentages or counts for hyperparameter tuning. For example, it states, 'In the LPW, the second scene and the third scene with a total of 1,975 people are used for training, and the first scene is tested with 756 people.' No dedicated 'validation' split is specified. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions `τ` as the margin of triplet loss but does not provide its specific value, nor other hyperparameters like learning rate, batch size, or optimizer settings. It states: 'Lt = [d(Fw(So i ), Fw(S+ i )) d(Fw(So i ), Fw(S i )) + τ]+ (3) where the d( ) is L2-norm distances, [ ]+ indicates max( , 0) and τ is the margin of triplet loss.' |