Deep Low-Resolution Person Re-Identification
Authors: Jiening Jiao, Wei-Shi Zheng, Ancong Wu, Xiatian Zhu, Shaogang Gong
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations show the advantages of our method over related state-of-the-art re-id and super-resolution methods on cross-resolution re-id benchmarks. We extensively performed comparative evaluations to show the superiority of our SING approach over related state-of-the-art re-id and image SR methods on four person re-id benchmarks CAVIAR (Cheng et al. 2011), VIPe R (Gray and Tao 2008), CUHK03 (Li et al. 2014), and SYSU (Chen et al. 2017a). Experiments Datasets. We performed evaluations on three simulated and one genuine LR person re-id datasets (Fig. 4). |
| Researcher Affiliation | Academia | Jiening Jiao,1,2 Wei-Shi Zheng,3,4 Ancong Wu,1 Xiatian Zhu,5 Shaogang Gong5 1 School of Electronics and Information Technology, Sun Yat-sen University, China 2 Collaborative Innovation Center of High Performance Computing, NUDT, China 3 School of Data and Computer Science, Sun Yat-sen University, China 4 Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University), Ministry of Education, China 5 School of Engineering and Computer Science, Queen Mary University of London, UK jiaojn@mail2.sysu.edu.cn, wszheng@ieee.org, wuancong@mail2.sysu.edu.cn, xiatian.zhu@qmul.ac.uk, s.gong@qmul.ac.uk |
| Pseudocode | No | The paper describes the network architecture but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | Datasets. We performed evaluations on three simulated and one genuine LR person re-id datasets (Fig. 4). (1) MLR-VIPe R was constructed from the VIPe R (Gray and Tao 2008) dataset. (2) MLR-SYSU is based on the SYSU dataset (Chen et al. 2017a). (3) MLR-CUHK03 was built from the CUHK03 (Li et al. 2014) dataset. (4) CAVIAR is a genuine LR person re-id dataset (Cheng et al. 2011). |
| Dataset Splits | Yes | All datasets except MLRCUHK03 were randomly divided into two halves, one for training and one for testing. That is, there are p = 25, 316 and 251 persons in the testing set of CAVIAR, MLR-VIPe R and MLR-SYSU, respectively. Following (Xiao et al. 2016), we utilised the benchmarking 1,367/100 training/test identity split. We repeated 10 times of the above random data split. The scaling parameter σ in Eq. (8) was set by cross validation on the validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of deep CNN models (SRCNN, DGD network) but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | In implementation, we upscale the LR images to an appropriate size (160 72 in our experiments) by bicubic interpolation as (Dong et al. 2016). We initialised the SR and Re ID sub-networks by SRCNN (Dong et al. 2016) pre-trained on Image Net-1K and DGD (Xiao et al. 2016) pre-trained on the training data of Market-1501 (Zheng et al. 2015), respectively. The scaling parameter σ in Eq. (8) was set by cross validation on the validation set. We set the balance coefficient α = 1 (Eq. (5)) which assumes equal importance between image SR and re-id feature learning. |