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..
Modeling Noisy Annotations for Crowd Counting
Authors: Jia Wan, Antoni Chan
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present experiments using our loss in (10) for training density map estimators. The experiments are conducted on 6 datasets: NWPU-Crowd [35], JHU-CROWD++ [36], UCF-QNRF [25], Shanghai_Tech [11], UCSD [6], and Mall [37]. |
| Researcher Affiliation | Academia | Jia Wan Antoni B. Chan Department of Computer Science City University of Hong Kong EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link or an explicit statement of code release. |
| Open Datasets | Yes | The experiments are conducted on 6 datasets: NWPU-Crowd [35], JHU-CROWD++ [36], UCF-QNRF [25], Shanghai_Tech [11], UCSD [6], and Mall [37]. |
| Dataset Splits | Yes | NWPU-CROWD is a large-scale benchmark for crowd counting which consists of 3,109 training images, 500 validation images and 1,500 testing images. JHU-CROWD++ has 4,371 images (2,722, 500, and 1,600 for train, val, test). UCF-QNRF contains 1,535 high-resolution images (1,201/334 for training/validation). Shanghai_Tech dataset consists of Part A and Part B. Part A has 482 and 300 images for training and evaluation, while Part B has 716 and 400 images for training and testing. For the datasets without a validation set, we use 10% of the images for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' and 'VGG19', 'CSRNet', 'MCNN' backbones, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We use Adam optimizer for training with learning rate 10 5. The regularization weight λ is set to 0.1. |