Distraction-Aware Feature Learning for Human Attribute Recognition via Coarse-to-Fine Attention Mechanism
Authors: Mingda Wu, Di Huang, Yuanfang Guo, Yunhong Wang12394-12401
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on the WIDER-Attribute and RAP databases, and state-of-the-art results are achieved, demonstrating the effectiveness of the proposed approach. |
| Researcher Affiliation | Academia | 1Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China 2IRIP Lab, School of Computer Science and Engineering, Beihang University, Beijing, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | Specifically, we train the segmentation network on the MS-COCO dataset, where FPN (Lin et al. 2017) is employed as the backbone network. |
| Dataset Splits | No | If the validation set is added to the training one as (Zhu et al. 2017a) does, an m AP of 87.2%/88.0% is obtained. |
| Hardware Specification | Yes | Our model is trained on a single NVIDIA 1080Ti GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software dependencies such as libraries, frameworks, or solvers. |
| Experiment Setup | Yes | The stochastic gradient descent algorithm is utilized in the training process, with a batch size of 32, a momentum of 0.9 and a weight decay of 0.0005. The initial learning rate is set to 0.003, and gamma is set to 0.1. |