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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Recurrent Attention Model for Pedestrian Attribute Recognition
Authors: Xin Zhao, Liufang Sang, Guiguang Ding, Jungong Han, Na Di, Chenggang Yan9275-9282
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical evidence shows that our recurrent model frameworks achieve state-of-the-art results, based on pedestrian attribute datasets, i.e. standard PETA and RAP datasets. and Experiment Datasets For evaluations, we use the two largest publicly available pedestrian attribute datasets: (1) The PEdes Train Attribute (PETA)... (2) The Richly Annotated Pedestrian (RAP)... and Results. The experiment results of our method and competitors are in Tab.3. |
| Researcher Affiliation | Academia | 1Beijing National Research Center for Information Science and Technology(BNRist) School of Software, Tsinghua University, Beijing 100084, China 2School of Computing & Communications, Lancaster University, UK 3Institute of Information and Control Hangzhou Dianzi University, Hangzhou, China |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | For evaluations, we use the two largest publicly available pedestrian attribute datasets: (1) The PEdes Train Attribute (PETA) (Deng et al. 2014) dataset consists of 19000 person images... (2) The Richly Annotated Pedestrian (RAP) attribute dataset (Li et al. 2016a) has 41585 images... |
| Dataset Splits | Yes | Following the same protocol as (Deng et al. 2015; Li, Chen, and Huang 2015), we divide the whole dataset into three non-overlapping partitions: 9500 for model training, 1900 for verification, and 7600 for model evaluation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper mentions 'tensorflow' but does not specify a version number or other software dependencies with specific versions. |
| Experiment Setup | Yes | The optimization algorithm used in training the proposed model is Adam. The initial learning rate of training is 0.1 and reduced to 0.001 by a factor of 0.1 at last. |