Transfer-learning for video classification: Video Swin Transformer on multiple domains
This addresses the problem of efficient video classification for computer vision researchers by showing transfer learning viability for similar domains but poor generalization to different class types, making it incremental in scope.
The paper investigated whether Video Swin Transformer (VST) generalizes to out-of-domain video classification using transfer learning from Kinetics-400, achieving 85% top-1 accuracy on FCVID (matching state-of-the-art) and 21% on Something-Something, with performance decreasing as video duration increases.
The computer vision community has seen a shift from convolutional-based to pure transformer architectures for both image and video tasks. Training a transformer from zero for these tasks usually requires a lot of data and computational resources. Video Swin Transformer (VST) is a pure-transformer model developed for video classification which achieves state-of-the-art results in accuracy and efficiency on several datasets. In this paper, we aim to understand if VST generalizes well enough to be used in an out-of-domain setting. We study the performance of VST on two large-scale datasets, namely FCVID and Something-Something using a transfer learning approach from Kinetics-400, which requires around 4x less memory than training from scratch. We then break down the results to understand where VST fails the most and in which scenarios the transfer-learning approach is viable. Our experiments show an 85\% top-1 accuracy on FCVID without retraining the whole model which is equal to the state-of-the-art for the dataset and a 21\% accuracy on Something-Something. The experiments also suggest that the performance of the VST decreases on average when the video duration increases which seems to be a consequence of a design choice of the model. From the results, we conclude that VST generalizes well enough to classify out-of-domain videos without retraining when the target classes are from the same type as the classes used to train the model. We observed this effect when we performed transfer-learning from Kinetics-400 to FCVID, where most datasets target mostly objects. On the other hand, if the classes are not from the same type, then the accuracy after the transfer-learning approach is expected to be poor. We observed this effect when we performed transfer-learning from Kinetics-400, where the classes represent mostly objects, to Something-Something, where the classes represent mostly actions.