Object Detection Algorithm Based on Context Information and Self-Attention Mechanism

Probabilistic Combination of Visual Context Based Attention and Object Detection

Visual context provides cues about an object’s presence, position and size within the observed scene, which are used to increase the performance of object detection techniques. However, state-of-theart methods for context aware object detection could decrease the initial performance. We discuss the reasons for failure and propose a concept that overcomes these limitations. Therefore, we introdu...

Attention-based Object Detection

Context Driven Focus of Attention for Object Detection

Context plays an important role in general scene perception. In particular, it can provide cues about an object’s location within an image. In computer vision, object detectors typically ignore this information. We tackle this problem by presenting a concept of how to extract and learn contextual information from examples. This context is then used to calculate a focus of attention, that repres...

Attention-Driven Parts-Based Object Detection

Recent studies have argued that natural vision systems perform classification by utilizing different mechanisms depending on the visual input. In this paper we present a hybrid, data-driven object detection system that combines parts-based matching and view-based attention for faster detection. We propose a simple competitive policy that allows incremental addition of new object classes to the ...

Anomaly-based Web Attack Detection: The Application of Deep Neural Network Seq2Seq With Attention Mechanism

Today, the use of the Internet and Internet sites has been an integrated part of the people’s lives, and most activities and important data are in the Internet websites. Thus, attempts to intrude into these websites have grown exponentially. Intrusion detection systems (IDS) of web attacks are an approach to protect users. But, these systems are suffering from such drawbacks as low accuracy in ...