Cross-View Projective Dictionary Learning for Person Re-Identification
Authors: Sheng Li, Ming Shao, Yun Fu
IJCAI 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the public VIPe R and CUHK Campus datasets show that our approach achieves the state-of-the-art performance. |
| Researcher Affiliation | Academia | Sheng Li Dept. of ECE Northeastern University Boston, MA, USA EMAIL Ming Shao Dept. of ECE Northeastern University Boston, MA, USA EMAIL Yun Fu Dept. of ECE and College of CIS Northeastern University Boston, MA, USA EMAIL |
| Pseudocode | Yes | Algorithm 1. CPDL for Person Re-identification |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for its source code. |
| Open Datasets | Yes | Experiments on the public VIPe R and CUHK Campus datasets show that our approach achieves the state-of-the-art performance. |
| Dataset Splits | Yes | We follow the evaluation protocol in [Gray and Tao, 2008]. In particular, we randomly select 316 pairs of images for training, and the remaining pairs are used for test. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | There are five parameters in our model, including α, β, λ, λ1 and λ2. In the experiments, we empirically set these parameters to achieve the best performance. In particular, α and β are set to 2 and 1, respectively. λ used in the fusion strategy is chosen in the range [0 1]. Two parameters λ1 and λ2 control the effects of cross-view interactions, and we will discuss their settings in the next section. ... We set λ1 = 1, λ2 = 2. |