# Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos

Source: [Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos](<https://www.sota2.com/research/paper/68fa6e4b1e209131cea3f0d2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:54:03.791Z

Authors: Zixiao Wang, Junwu Weng, Chun Yuan, Jue Wang

Published: 2022-12-27T14:09:14.000Z

## Abstract

Learning with noisy label (LNL) is a classic problem that has been extensively studied for image tasks, but much less for video in the literature. A straightforward migration from images to videos without considering the properties of videos, such as computational cost and redundant information, is not a sound choice. In this paper, we propose two new strategies for video analysis with noisy labels: 1) A lightweight channel selection method dubbed as Channel Truncation for feature-based label noise detection. This method selects the most discriminative channels to split clean and noisy instances in each category; 2) A novel contrastive strategy dubbed as Noise Contrastive Learning, which constructs the relationship between clean and noisy instances to regularize model training. Experiments on three well-known benchmark datasets for video classification show that our proposed tru{\\bf N}cat{\\bf E}-split-contr{\\bf A}s{\\bf T} (NEAT) significantly outperforms the existing baselines. By reducing the dimension to 10\\% of it, our method achieves over 0.4 noise detection F1-score and 5\\% classification accuracy improvement on Mini-Kinetics dataset under severe noise (symmetric-80\\%). Thanks to Noise Contrastive Learning, the average classification accuracy improvement on Mini-Kinetics and Sth-Sth-V1 is over 1.6\\%.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2212.13495>)

[PDF](<https://arxiv.org/pdf/2212.13495.pdf>)