Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos
Authors: Zixiao Wang, Junwu Weng, Chun Yuan, Jue Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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. |