Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Venue PDF | LLM Run Details | Input Tokens: 17,152 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,449 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |