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..
Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos
Authors: Zixiao Wang, Junwu Weng, Chun Yuan, Jue Wang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three well-known benchmark datasets for video classification show that our proposed tru Ncat E-split-contr As T (NEAT) significantly outperforms the existing baselines. |
| Researcher Affiliation | Collaboration | Zixiao Wang1*, Junwu Weng2, Chun Yuan1, Jue Wang2 1 Tsinghua University 2 Tencent AI Lab |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | We conduct experiments on three large-scale video classification datasets, Kinetics-400 (K400), Mini-kinetics (K200), and Something-Something-V1 (Sth V1). |
| Dataset Splits | Yes | K400 This is a large-scale action recognition dataset with 400 categories, and it contains 240k videos for training, and 20k (50 per category) for validation. ... K200 This is a balanced subset of K400 with 200 categories. In this dataset, each category contains 400 videos for training and 25 videos for validation. ... Sth V1 Something-Something is a challenging dataset focusing on temporal reasoning. ... It contains 86k videos for training and 12k for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | All training hyper-parameters are adapted from the original work (Lin, Gan, and Han 2019) and detailed in Appendix. Following previous work, we choose τ = 0.1 for all experiments in this paper. A memory bank with the size of 16 K is added for all contrastive learning loss to provide ample training positive-negative pairs. |