Applying multi-scale representations leads to consistent performance improvements on a wide range of image recognition tasks. However, with the addition of the temporal dimension in video domain, directly obtaining layer-wise multi-scale spatial-temporal features will add a lot extra computational cost. In this work, we propose a novel and efficient Multi-Scale Spatial-Temporal Integration Convolutional Tube (MSTI) aiming at achieving accurate recognition of actions with lower computational cost. It firstly extracts multi-scale spatial and temporal features through the multi-scale convolution block. Considering the interaction of different-scales representations and the interaction of spatial appearance and temporal motion, we employ the cross-scale attention weighted blocks to perform feature recalibration by integrating multi-scale spatial and temporal features. An end-to-end deep network, MSTI-Net, is also presented based on the proposed MSTI tube for human action recognition. Extensive experimental results show that our MSTI-Net significantly boosts the performance of existing convolution networks and achieves state-of-the-art accuracy on three challenging benchmarks, i.e., UCF-101, HMDB-51 and Kinetics-400, with much fewer parameters and FLOPs.

The attention mechanism has been widely applied to enhance pedestrian representation for person re-identification in videos. However, most existing methods learn the spatial and temporal attention separately, and thus ignore the correlation between them. In this work, we propose a novel Adaptive Spatio-Temporal Attention Network (ASTA-Net) to adaptively aggregate the spatial and temporal attention features into discriminative pedestrian representation for person re-identification in videos. Specifically, multiple Adaptive Spatio-Temporal Fusion modules within ASTA-Net are designed for exploring precise spatio-temporal attention on multi-level feature maps. They first obtain the preliminary spatial and temporal attention features via the spatial semantic relations for each frame and temporal dependencies among inconsecutive frames, then adaptively aggregate the preliminary attention features on the basis of their correlation. Moreover, an Adjacent-Frame Motion module is designed to explicitly extract motion patterns according to the feature-level variation among adjacent frames. Extensive experiments on the three widely-used datasets, i.e., MARS, iLIDS-VID and PRID2011, have demonstrated the effectiveness of the proposed approach.

Recent works use 3D convolutional neural networks to explore spatio-temporal information for human action recognition. However, they either ignore the correlation between spatial and temporal features or suffer from high computational cost by spatio-temporal features extraction. In this work, we propose a novel and efficient Mutually Reinforced Spatio-Temporal Convolutional Tube (MRST) for human action recognition. It decomposes 3D inputs into spatial and temporal representations, mutually enhances both of them by exploiting the interaction of spatial and temporal information and selectively emphasizes informative spatial appearance and temporal motion, meanwhile reducing the complexity of structure. Moreover, we design three types of MRSTs according to the different order of spatial and temporal information enhancement, each of which contains a spatio-temporal decomposition unit, a mutually reinforced unit and a spatio-temporal fusion unit. An end-to-end deep network, MRST-Net, is also proposed based on the MRSTs to better explore spatio-temporal information in human actions. Extensive experiments show MRST-Net yields the best performance, compared to state-of-the-art approaches.

The 3D convolutional neural networks recently have been applied to explore spatial-temporal content for video action recognition. However, they either suffer from high computational cost by spatial-temporal feature extraction or ignore the correlation between appearance and motion. In this work, we propose a novel Cross-Fiber Spatial-Temporal Co-enhanced (CFST) architecture aiming to reduce the number of parameters tremendously while achieve accurate recognition of actions. We slice the complex 3D convolutional network into a group of lightweight fibers that run through the whole network. Crossing separated fibers, we introduce the Cross-Fiber Recalibration unit which shares extracted features from each fiber and measures the interaction between fibers to emphasize informative ones. Within each fiber, the Spatial-Temporal Co-enhanced unit is put forward to co-enhance the learning of spatial and temporal features, leading to more discriminative spatial-temporal representation. An end-to-end deep network, CFST-Net, is also presented based on the proposed CFST architecture for video action recognition. Extensive experimental results show that our CFST-Net significantly boosts the performance of existing convolution networks and achieves state-of-the-art accuracy on three challenging benchmarks, i.e., UCF-101, HMDB-51 and Kinetics-400, with much fewer parameters and FLOPs.