Discriminative Semi-Coupled Projective Dictionary Learning for Low-Resolution Person Re-Identification

Authors: Kai Li, Zhengming Ding, Sheng Li, Yun Fu

AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experiments on three public datasets show the superiority of the proposed method to the state-of-the-art ones.
Researcher Affiliation Collaboration Department of Electrical & Computer Engineering, Northeastern University, Boston, USA Adobe Research, USA College of Computer & Information Science, Northeastern University, Boston, USA
Pseudocode Yes Algorithm 1: DSPDL for person re-ID.
Open Source Code No The paper does not provide an explicit statement or link for open-source code for the described methodology.
Open Datasets Yes We employed three datasets for performance evaluation: the VIPe R dataset (Gray and Tao 2008), the CUHK01 dataset (Zhao, Ouyang, and Wang 2014), and the QMULi LIDS dataset (Zheng, Gong, and Xiang 2009).
Dataset Splits No The paper mentions splitting data into training and testing parts but does not specify a separate validation split: 'all the pedestrian pairs were randomly divided into two equal parts, with one part serving for training and the other for testing.'
Hardware Specification No The paper does not specify the hardware (e.g., GPU models, CPU types) used for running the experiments.
Software Dependencies No The paper does not provide specific version numbers for software dependencies or libraries used in the implementation.
Experiment Setup Yes There are two major parameters in our algorithm: λ1 and λ2. λ2 controls the scale of a variable; thus we set it empirically as a small value λ2 = 0.01. ... DSPDL reaches the best performances when λ1 = 0.1. Thus, λ1 was set as 0.1 in our method as default. ... we down-sampled all images from one view with the rate 1/8, and kept images from the other view unchanged to simulate the great resolution differences.