Nowadays, deep learning has demonstrated impressive performance in the area of computer vision and pattern recognition, such as objects recognition, videos classification and image segmentation. In particular, convolutional neural networks (CNNs) an advanced deep-learning technique have achieved strong performance in image and video analysis owing to their powerful feature-extraction capabilities.Based on that, current research also make a breakthrough in image and video recognition via aggregating features extracted from various layers of CNNs. Over the last decade, feature fusion strategies have developed from conventional schemes restricted to conditions we need to control over advanced ones which can be achieved with a large number of images or videos. However, due to the rapid development in this field, it is challenging to track and systematically analyze recent advancements. This has inspired us to offer a comprehensive survey of the significant steps taken towards feature aggregation strategies. To better organize and facilitate understanding, we first introduce preliminary knowledge on deep learning. Then this paper focuses on categorizing and reviewing the current strategies from two main aspects: feature aggregation on images and feature aggregation on videos. We further highlight the comparative strengths and limitations among these strategies. Finally, we point out the challenges in this area and motivate further work via proposing future directions on feature aggregation techniques for both images and videos.

Social networks are important media for spreading information, ideas, and influence among individuals. Most existing research focuses on understanding the characteristics of social networks, investigating how information is spread through the "word-of-mouth" effect of social networks, or exploring social influences among individuals and groups. However, most studies ignore negative influences among individuals and groups. Motivated by the goal of alleviating social problems, such as drinking, smoking, and gambling, and influence-spreading problems, such as promoting new products, we consider positive and negative influences, and propose a new optimization problem called the Minimum-sized Positive Influential Node Set (MPINS) selection problem to identify the minimum set of influential nodes such that every node in the network can be positively influenced by these selected nodes with no less than a threshold of $\theta$. Our contributions are threefold. First, we prove that, under the independent cascade model considering positive and negative influences, MPINS is APX-hard. Subsequently, we present a greedy approximation algorithm to address the MPINS selection problem. Finally, to validate the proposed greedy algorithm, we conduct extensive simulations and experiments on random graphs and seven different real-world data sets that represent small-, medium-, and large-scale networks.