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..
Instance Mining with Class Feature Banks for Weakly Supervised Object Detection
Authors: Yufei Yin, Jiajun Deng, Wengang Zhou, Houqiang Li3190-3198
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on two publicly available datasets, Pascal VOC 2007 and 2012, demonstrate the effectiveness of our method. |
| Researcher Affiliation | Academia | 1 CAS Key Laboratory of GIPAS, EEIS Department, University of Science and Technology of China 2 Institute of Artificial Intelligence, Hefei Comprehensive National Science Center |
| Pseudocode | Yes | Algorithm 1: CFB updating strategy |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | We evaluate our proposed method on both Pascal VOC 2007 and Pascal VOC 2012 (Everingham et al. 2010) following previous WSOD works. |
| Dataset Splits | Yes | We train on trainval split (5,011 images for VOC 2007, 11,540 images for VOC 2012) |
| Hardware Specification | Yes | Our experiments are implemented based on Py Torch on NVIDIA GTX 1080Ti GPUs. |
| Software Dependencies | No | The paper mentions "Py Torch" but does not specify a version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The batch size, momentum, and weight decay are set to 4, 0.9, and 5 10 4 respectively. The learning rate is set to 1 10 3 for the first 60K iterations and 1 10 4 for the following 20K iterations. For data augmentation, we use five image scales, i.e., {480, 576, 688, 864, 1200} for the shortest side of images, and random horizontal flipping is applied. We set K = 6 for the VOC 2007 and K = 8 for the VOC 2012. We set τ = 0.5 for both datasets. Considering that the stability of the network increases during the training process, we set α = 5 at the first 40K iterations and then tighten the restriction with α = 2. The number of refinement branches T is set to 2. |