Recurrent Attention Model for Pedestrian Attribute Recognition
Authors: Xin Zhao, Liufang Sang, Guiguang Ding, Jungong Han, Na Di, Chenggang Yan9275-9282
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 veriļ¬cation, 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. |