SOTAVerified

Mutual Modality Learning for Video Action Classification

2020-11-04Code Available1· sign in to hype

Stepan Komkov, Maksim Dzabraev, Aleksandr Petiushko

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Abstract

The construction of models for video action classification progresses rapidly. However, the performance of those models can still be easily improved by ensembling with the same models trained on different modalities (e.g. Optical flow). Unfortunately, it is computationally expensive to use several modalities during inference. Recent works examine the ways to integrate advantages of multi-modality into a single RGB-model. Yet, there is still a room for improvement. In this paper, we explore the various methods to embed the ensemble power into a single model. We show that proper initialization, as well as mutual modality learning, enhances single-modality models. As a result, we achieve state-of-the-art results in the Something-Something-v2 benchmark.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
Something-Something V2MML (ensemble)Top-1 Accuracy69.02Unverified
Something-Something V2MML (single)Top-1 Accuracy66.83Unverified

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