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..
Attribute Aware Pooling for Pedestrian Attribute Recognition
Authors: Kai Han, Yunhe Wang, Han Shu, Chuanjian Liu, Chunjing Xu, Chang Xu
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmark datasets demonstrate that the proposed pooling method appropriately explores and exploits the correlations between attributes for the pedestrian attribute recognition. |
| Researcher Affiliation | Collaboration | Kai Han1, Yunhe Wang1, Han Shu1, Chuanjian Liu1, Chunjing Xu1, Chang Xu2 1Huawei Noah s Ark Lab 2School of Computer Science, FEIT, University of Sydney, Australia |
| Pseudocode | Yes | Algorithm 1 Attribute aware pooling method for pedestrain attribute classification with CNNs. |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | The evaluation is conducted on three largest publicly available pedestrian attribute datasets: PETA [Deng et al., 2014], RAP [Li et al., 2016], and PA-100K [Liu et al., 2017b] |
| Dataset Splits | Yes | PETA [Deng et al., 2014]: ...we randomly divide the whole dataset into three partitions: 9,500 images for training, 1,900 for validation, and 7,600 for testing... PA-100K [Liu et al., 2017b]: ...The whole dataset is randomly split into training, validation and test sets with a ratio of 8 : 1 : 1. |
| Hardware Specification | Yes | All methods were implemented using Py Torch [Paszke et al., 2017] and run on NVIDIA V100 graphics cards. |
| Software Dependencies | No | All methods were implemented using Py Torch [Paszke et al., 2017] and run on NVIDIA V100 graphics cards. While PyTorch is mentioned, a specific version number is not provided, which is required for reproducibility. |
| Experiment Setup | Yes | Adam optimizer [Kingma and Ba, 2014] with a batch size of 32 is used to fine-tune the entire model with an initial learning rate of 0.01. The images are resized to 512 × 256 and during training randomly cropped to the size of 448 × 224 with random horizontal flipping. We tested the impact of λ by tuning it from 0 to 0.5 with the step of 0.05 on the m A metric using the PETA dataset. |