  Learning visual knowledge from massive weakly-labeled web videos has
attracted growing research interests thanks to the large corpus of easily
accessible video data on the Internet. However, for video action recognition,
the action of interest might only exist in arbitrary clips of untrimmed web
videos, resulting in high label noises in the temporal space. To address this
issue, we introduce a new method for pre-training video action recognition
models using queried web videos. Instead of trying to filter out, we propose to
convert the potential noises in these queried videos to useful supervision
signals by defining the concept of Sub-Pseudo Label (SPL). Specifically, SPL
spans out a new set of meaningful "middle ground" label space constructed by
extrapolating the original weak labels during video querying and the prior
knowledge distilled from a teacher model. Consequently, SPL provides enriched
supervision for video models to learn better representations. SPL is fairly
simple and orthogonal to popular teacher-student self-training frameworks
without extra training cost. We validate the effectiveness of our method on
four video action recognition datasets and a weakly-labeled image dataset to
study the generalization ability. Experiments show that SPL outperforms several
existing pre-training strategies using pseudo-labels and the learned
representations lead to competitive results when fine-tuning on HMDB-51 and
UCF-101 compared with recent pre-training methods.
