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..
Submodular Attribute Selection for Action Recognition in Video
Authors: Jingjing Zheng, Zhuolin Jiang, Rama Chellappa, Jonathon P Phillips
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the Olympic Sports and UCF101 datasets demonstrate that the proposed attribute-based representation can significantly boost the performance of action recognition algorithms and outperform most recently proposed recognition approaches. |
| Researcher Affiliation | Collaboration | Jinging Zheng UMIACS, University of Maryland College Park, MD, USA EMAIL; Zhuolin Jiang Noah s Ark Lab Huawei Technologies EMAIL; Rama Chellappa UMIACS, University of Maryland College Park, MD, USA EMAIL; P. Jonathon Phillips National Institute of Standards and Technology Gaithersburg, MD, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Submodular Attribute Selection |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | In this section, we validate our method for action recognition on two public datasets: Sports dataset [25] and UCF101 [20] dataset. |
| Dataset Splits | Yes | Following the training and testing dataset partitions proposed in [30], we train a linear SVM and report classification accuracies of different attribute-based representations in Table 1. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions using an SVM and other techniques like KSVD and PCA, but it does not provide specific version numbers for any software, libraries, or frameworks used. |
| Experiment Setup | Yes | Figure 4d shows the performance curves for a range of λ. We observe that the combination of entropy rate term and maximum coverage term obtains a higher classification accuracy than when only one of them is used. In addition, our approach is insensitive to the selection of λ. Hence we use λ = 0.1 throughout the experiments. |