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.
Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles
Authors: Dahun Kim, Donghyeon Cho, In So Kweon8545-8552
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,156 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,959 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 | In experiments, we demonstrate that our learned 3D representation is well transferred to action recognition tasks, and outperforms state-of-the-art 2D CNN-based competitors on UCF101 and HMDB51 datasets. |
| Researcher Affiliation | Academia | Dahun Kim, Donghyeon Cho, In So Kweon Dept. of Electrical Engineering, KAIST, Daejeon, Korea EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any sections explicitly labeled as 'Pseudocode' or 'Algorithm', nor are there structured code-like blocks. |
| Open Source Code | No | All the pre-trained models and the source codes will be available soon. |
| Open Datasets | Yes | We conduct video recognition experiments on two benchmark action recognition datasets, namely UCF101 (Soomro, Zamir, and Shah 2012) and HMDB51 (Kuehne et al. 2011). |
| Dataset Splits | Yes | All the experiments follow the training/test splits of UCF101 and HMDB51, and we mostly report the average classification accuracy over the three splits for UCF101, as done in (Hara, Kataoka, and Satoh 2018). |
| Hardware Specification | Yes | We use stochastic gradient descent with a momentum of 0.9 on two GTX-1080Ti GPUs. |
| Software Dependencies | No | The paper mentions models like '3D Res Net' and datasets like 'Kinetics', but does not specify software components (e.g., programming languages, libraries, frameworks) with version numbers used for the experiments. |
| Experiment Setup | Yes | We set the mini-batch size as 128 and the initial learning rate as 0.01. We start from a learning rate of 0.05, and assign a weight decay of 5e-4. |