  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\%.
