Object detection based on an adaptive attention mechanism

Attention-based Object Detection

Adaptive Visual Attention Based Object Recognition

Detecting and identifying objects, in particular the recognition of 3D objects, are important capabilities for robots performing non-trivial tasks in real world environments. In order to be able to solve object related problems the robot has to localise objects of interest in complex visual scenes and has to identify or categorise certain task-relevant objects. When performing tasks in complex ...

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 ...

Adaptive object detection based on modified Hebbian learning

a b d This paper focuses on the issue of developing self-adapting automatic object detection systems for improving their performance. ’ b o general methodologies for performance improvement are first introduced. They are based on parameter optimizing and input adapting. Different modified Hebbian learning rules are developed to build adaptive feature extractors which transform the input data in...