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..
Self-Supervised Video Representation Learning via Latent Time Navigation
Authors: Di Yang, Yaohui Wang, Quan Kong, Antitza Dantcheva, Lorenzo Garattoni, Gianpiero Francesca, François Brémond
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experimental analysis suggests that learning video representations by LTN consistently improves performance of action classification in fine-grained and human-oriented tasks (e.g., on Toyota Smarthome dataset). In addition, we demonstrate that our proposed model, when pre-trained on Kinetics-400, generalizes well onto the unseen real world video benchmark datasets UCF101 and HMDB51, achieving state-of-the-art performance in action recognition. |
| Researcher Affiliation | Collaboration | 1Inria, 2004 Rte des Lucioles, Valbonne, France 2Universit e Cˆote d Azur, 28 Av. de Valrose, Nice, France 3Toyota Motor Europe, 60 Av. du Bourget, Brussels, Belgium 4Woven Planet Holdings, 3-2-1 Nihonbashimuromachi, Chuo-ku, Tokyo, Japan 5Shanghai AI Laboratory, 701 Yunjin Road, Shanghai, China |
| Pseudocode | No | The paper describes the proposed approach through text and diagrams (Figure 2) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for the source code of the described methodology. |
| Open Datasets | Yes | We conduct extensive experiments to evaluate LTN on four action classification datasets: Toyota Smarthome, Kinetics400, UCF101 and HMDB51. |
| Dataset Splits | No | The paper mentions using datasets like Smarthome Cross-Subject, Kinetics-400, UCF101, and HMDB51, and discusses evaluation protocols (e.g., linear evaluation, fine-tuning), but does not explicitly provide specific percentages or sample counts for training, validation, and test splits within its text. |
| Hardware Specification | No | The paper states 'This work was granted access to the HPC resources of IDRIS under the allocation AD011011627R1,' but it does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | For the proposed Dt, unless otherwise stated, we set M = 64 directions over the dim = 2048 dimensions... The results shown in Tab. 3 suggest that 2-layer MLP with 2048 dimensions in the hidden layer is the most effective. |