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..
VideoCapsuleNet: A Simplified Network for Action Detection
Authors: Kevin Duarte, Yogesh Rawat, Mubarak Shah
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed network achieves state-of-the-art performance on multiple action detection datasets including UCF-Sports, J-HMDB, and UCF-101 (24 classes) with an impressive 20% improvement on UCF-101 and 15% improvement on J-HMDB in terms of v-m AP scores. |
| Researcher Affiliation | Academia | Kevin Duarte EMAIL Yogesh S Rawat EMAIL Mubarak Shah EMAIL Center for Research in Computer Vision University of Central Florida Orlando, FL 32816 |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| 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 measure the performance of our network on three datasets UCF-Sports [15], J-HMDB [16], UCF-101 [17]. |
| Dataset Splits | Yes | The UCF-Sports dataset consists of 150 videos from 10 action classes. All videos contain spatio-temporal annotations in the form of frame-level bounding boxes and we follow the standard training/testing split used by [21]. |
| Hardware Specification | Yes | Although capsule networks tend to be computationally expensive (due to the routing-by-agreement), capsule-pooling allows Video Capsule Net to run on a single Titan X GPU using a batch size of 8. |
| Software Dependencies | No | We implement Video Capsule Net using Tensor๏ฌow [12]. |
| Experiment Setup | Yes | The network was trained using the Adam optimizer [14], with a learning rate of 0.0001. Due to the size of the Video Capsule Net, a batch size of 8 was used during training. |