Hybrid and hierarchical fusion networks: a deep cross-modal learning architecture for action recognition
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Two-stream networks have provided an alternate way of exploiting the spatiotemporal information for action recognition problem. Nevertheless, most of the two-stream variants perform the fusion of homogeneous modalities which cannot efficiently capture the action-motion dynamics from the videos. Moreover, the existing studies cannot extend the streams beyond the number of modalities. To address these limitations, we propose a hybrid and hierarchical fusion (HHF) networks. The hybrid fusion handles non-homogeneous modalities and introduces a cross-modal learning stream for effective modeling of motion dynamics while extending the networks from existing two-stream variants to three and six streams. On the other hand, the hierarchical fusion makes the modalities consistent by modeling long-term temporal information along with the combination of multiple streams to improve the recognition performance. The proposed network architecture comprises of three fusion tiers: the hybrid fusion itself, the long-term fusion pooling layer which models the long-term dynamics from RGB and optical flow modalities, and the adaptive weighting scheme for combining the classification scores from several streams. We show that the hybrid fusion has different representations from the base modalities for training the cross-modal learning stream. We have conducted extensive experiments and shown that the proposed six-stream HHF network outperforms the existing two- and four-stream networks, achieving the state-of-the-art recognition performance, 97.2% and 76.7% accuracies on UCF101 and HMDB51 datasets, respectively, which are widely used in action recognition studies.
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