Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Video-Based Person Re-Identification by Simultaneously Learning Intra-Video and Inter-Video Distance Metrics
Authors: Xiaoke Zhu, Xiao-Yuan Jing, Fei Wu, Hui Feng
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the public i LIDS-VID and PRID 2011 image sequence datasets show that our approach achieves the stateof-the-art performance. |
| Researcher Affiliation | Academia | Xiaoke Zhu1,3, Xiao-Yuan Jing ,1,2, Fei Wu2,1, Hui Feng1 1State Key Laboratory of Software Engineering, School of Computer, Wuhan University, China 2College of Automation, Nanjing University of Posts and Telecommunications, China 3 School of Computer and Information Engineering, Henan University, China |
| Pseudocode | Yes | Algorithm 1 Simultaneous intra-video and inter-video distance learning (SI2DL) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | To evaluate the effectiveness of our approach, we conduct extensive experiments on two publicly available person sequence datasets, including i LIDS-VID [Wang et al., 2014] and PRID 2011 [Hirzer et al., 2011]. |
| Dataset Splits | Yes | In experiments, we choose these parameters by 5-fold cross-validation on each dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, 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 | Yes | Parameter Settings. There are three parameters in our SI2DL model, i.e., µ, 1 and 2. In experiments, we choose these parameters by 5-fold cross-validation on each dataset. With respect to K1 and K2, we set them as (2200, 80) for i LIDS-VID, and (2500, 100) for PRID 2011, respectively. ... The parameters µ, 1 and 2 are set as 0.00005, 0.2 and 0.2, respectively. ... The parameters µ, 1 and 2 are set as 0.00005, 0.1 and 0.1, respectively. |