Video action recognition, which is topical in computer vision and video analysis, aims to allocate a short video clip to a pre-defined category such as brushing hair or climbing stairs. Recent works focus on action recognition with deep neural networks that achieve state-of-the-art results in need of high-performance platforms. Despite the fast development of mobile computing, video action recognition on mobile devices has not been fully discussed. In this paper, we focus on the novel mobile video action recognition task, where only the computational capabilities of mobile devices are accessible. Instead of raw videos with huge storage, we choose to extract multiple modalities (including I-frames, motion vectors, and residuals) directly from compressed videos. By employing MobileNetV2 as backbone, we propose a novel Temporal Trilinear Pooling (TTP) module to fuse the multiple modalities for mobile video action recognition. In addition to motion vectors, we also provide a temporal fusion method to explicitly induce the temporal context. The efficiency test on a mobile device indicates that our model can perform mobile video action recognition at about 40FPS. The comparative results on two benchmarks show that our model outperforms existing action recognition methods in model size and time consuming, but with competitive accuracy.
Pooling methods are requisite either in two-stream networks @cite_32 @cite_33 or in other feature fusion models. @cite_9 simply uses average pooling and outperforms others. @cite_28 proposes bilinear pooling to model local parts of object: two feature representations are learned separately and then multiplied using the outer product to obtain the holistic representation. @cite_23 combines two-stream network with a compact bilinear representation @cite_4 . @cite_30 defines a general kernel-based pooling framework which captures higher-order interactions of features. However, most existing bilinear pooling models are capable to combine only two features, and none of their variants could cope with more than two features, which is needed in video action recognition.
@cite_30 @cite_4 @cite_33 @cite_28 @cite_9 @cite_32 @cite_23
2740620254 2963066927 2104657103 2507009361 2156303437 2736596806
View-based methods have achieved considerable success in 3D object recognition tasks. Different from existing view-based methods pooling the view-wise features, we tackle this problem from the perspective of patches-to-patches similarity measurement. By exploiting the relationship between polynomial kernel and bilinear pooling, we obtain an effective 3D object representation by aggregating local convolutional features through bilinear pooling. Meanwhile, we harmonize different components inherited in the bilinear feature to obtain a more discriminative representation. To achieve an end-to-end trainable framework, we incorporate the harmonized bilinear pooling as a layer of a network, constituting the proposed Multi-view Harmonized Bilinear Network (MHBN). Systematic experiments conducted on two public benchmark datasets demonstrate the efficacy of the proposed methods in 3D object recognition. In this paper, we present an analysis of different multimodal fusion approaches in the context of deep learning, focusing on pooling intermediate representations learned for the acoustic and lexical modalities. Traditional approaches to multimodal feature pooling include: concatenation, element-wise addition, and element-wise multiplication. We compare these traditional methods to outer-product and compact bilinear pooling approaches, which consider more comprehensive interactions between features from the two modalities. We also study the influence of each modality on the overall performance of a multimodal system. Our experiments on the IEMOCAP dataset suggest that: (1) multimodal methods that combine acoustic and lexical features outperform their unimodal counterparts; (2) the lexical modality is better for predicting valence than the acoustic modality; (3) outer-product-based pooling strategies outperform other pooling strategies. Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition. However, bilinear features are high dimensional, typically on the order of hundreds of thousands to a few million, which makes them impractical for subsequent analysis. We propose two compact bilinear representations with the same discriminative power as the full bilinear representation but with only a few thousand dimensions. Our compact representations allow back-propagation of classification errors enabling an end-to-end optimization of the visual recognition system. The compact bilinear representations are derived through a novel kernelized analysis of bilinear pooling which provide insights into the discriminative power of bilinear pooling, and a platform for further research in compact pooling methods. Experimentation illustrate the utility of the proposed representations for image classification and few-shot learning across several datasets. Convolutional Neural Networks (CNNs) with Bilinear Pooling, initially in their full form and later using compact representations, have yielded impressive performance gains on a wide range of visual tasks, including fine-grained visual categorization, visual question answering, face recognition, and description of texture and style. The key to their success lies in the spatially invariant modeling of pairwise (2nd order) feature interactions. In this work, we propose a general pooling framework that captures higher order interactions of features in the form of kernels. We demonstrate how to approximate kernels such as Gaussian RBF up to a given order using compact explicit feature maps in a parameter-free manner. Combined with CNNs, the composition of the kernel can be learned from data in an end-to-end fashion via error back-propagation. The proposed kernel pooling scheme is evaluated in terms of both kernel approximation error and visual recognition accuracy. Experimental evaluations demonstrate state-of-the-art performance on commonly used fine-grained recognition datasets.