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The emergence of wearable devices has opened up new potentials for egocentric activity recognition. Although some methods integrate attention mechanisms into deep neural networks to capture fine-grained human-object interactions in a weak-supervision manner, they either ignore exploiting the temporal consistency or generate attention based on considering appearance cues only. To address these limitations, in this paper, we propose an enhanced attention-tracking method, combined with multi-branch network (EAT-MBNet), for egocentric activity recognition. Specifically, we propose class-aware attention maps (CAAMs) by employing a self-attention-based module to refine the class activation maps (CAMs). Our proposed method can enhance the semantic dependency between the activity categories and the feature maps. To highlight the discriminative features from the regions of interest across frames, we propose a flow-guided attention-tracking (F-AT) module, by simultaneously leveraging historical attention and motion patterns. Furthermore, we propose a cross-modality modeling branch based on an interactive GRU module, which captures the time-synchronized long-term relationships between the appearance and motion branches. Experimental results on four egocentric activity benchmarks demonstrate that the proposed method achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

The stable continuation of trajectories among different targets has always been the key to the tracking performance of multi-object tracking (MOT) tasks. If features of the target are aggregated and classified simply, the discriminant features of the target will be ignored. This will affect the robustness of the trajectory generated by the model. Meanwhile, many popular models are keen to execute detection and feature extraction tasks in parallel. But these two tasks will conflict with each other when optimized respectively. Therefore, we propose our tracker MOTFR to solve the above problems. In this paper, we propose a Locally Shared Information Decoupling Module (LSIDM) to reduce task optimization conflicts while ensuring the necessary information sharing. Meanwhile, a feature recoding module for deep extraction of identity discriminative features is proposed, which is called the Feature Purification Module (FPM). By combining LSIDM and FPM modules, the model utilizes the discriminative appearance features to guide the optimization of detection and further improves the performance of our model. To solve the problem of targets disappearing due to various abnormal occlusion, a Short-term Trajectory Online Complement Strategy (STOCS) is proposed to realize the trajectories continuation of these targets in the tracking stage. Through sufficient experiments, we demonstrate the superior performance of our MOTFR, which guarantees high-quality detection while achieving the stability of the target trajectory. [ABSTRACT FROM AUTHOR]

The two-stream convolutional network has been proved to be one milestone in the study of video-based action recognition. Lots of recent works modify internal structure of two-stream convolutional network directly and put top-level features into a 2D/3D convolution fusion module or a simpler one. However, these fusion methods cannot fully utilize features and the way fusing only top-level features lacks rich vital details. To tackle these issues, a novel network called Diverse Features Fusion Network (DFFN) is proposed. The fusion stream of DFFN contains two types of uniquely designed modules, the diverse compact bilinear fusion (DCBF) module and the channel-spatial attention (CSA) module, to distill and refine diverse compact spatiotemporal features. The DCBF modules use the diverse compact bilinear algorithm to fuse features extracted from multiple layers of the base network that are called diverse features in this paper. Further, the CSA module leverages channel attention and multi-size spatial attention to boost key information as well as restraining the noise of fusion features. We evaluate our three-stream network DFFN on three public challenging video action benchmarks: UCF101, HMDB51 and Something-Something V1. Experiment results indicate that our method achieves state-of-the-art performance. • Diverse compact bilinear fusion modules fuse spatial features and temporal features. • Diverse compact bilinear fusion modules fuse diverse features from different layers. • The multi-size spatial-channel attention module can adjust the attention of network. • The proposed network achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

As a challenging task of video classification, action recognition has become a significant topic of computer vision community. The most popular methods based on two-stream architecture up to now are still simply fusing the prediction scores of each stream. In that case, the complementary characteristics of two streams cannot be fully utilized and the effect of shallower features is often overlooked. In addition, the equal treatment to features may weaken the role of the feature contributing significantly to the classification. Accordingly, a novel network called Multiple Depth-levels Features Fusion Enhanced Network (MDFFEN) is proposed. It improves on two aspects of two-stream architecture. In terms of the two-stream interaction mechanism, multiple depth-levels features fusion (MDFF) is formed to aggregate spatial–temporal features extracted from several sub-modules of original two streams by spatial–temporal features fusion (STFF). And with respect to further refining the spatiotemporal features, we propose a group-wise spatial-channel enhance (GSCE) module to highlight the meaningful regions and expressive channels automatically by priority assignment. The competitive results are achieved after we validate MDFFEN on three public challenging action recognition datasets, HDMB51, UCF101 and ChaLearn LAP IsoGD. • The proposed Two-Stream Features Fusion extracts diverse hybrid features. • The fusion of three types of features learns more discriminative information. • The Group-wise Spatial-Channel Enhance mechanism reduces interference information. • The proposed network achieves competitive performance. [ABSTRACT FROM AUTHOR]