In object detection, non-maximum suppression (NMS) methods are extensively adopted to remove horizontal duplicates of detected dense boxes for generating final instances. However, due the degraded quality detection and not explicit exploration context information, existing NMS via simple intersection-over-union (IoU) metrics tend underperform on multi-oriented long-size objects detection. Disti...

This paper introduces a novel multi-view 6 DoF object pose refinement approach focusing on improving methods trained synthetic data. It is based the DPOD detector, which produces dense 2D-3D correspondences between model vertices and image pixels in each frame. We have opted for use of multiple frames with known relative camera transformations, as it allows introduction geometrical constraints ...

In real applications, new object classes often emerge after the detection model has been trained on a prepared dataset with fixed classes. Fine-tuning old only data will lead to well-known phenomenon of catastrophic forgetting, which severely degrades performance modern detectors. Due storage burden, privacy and time consumption, sometimes it is impractical train from scratch all both this pape...

In this paper, we present a multi-formalism modeling framework (abbreviated by MFMF) for modeling and simulation. The proposed framework is defined based on the concepts of meta-models and uses object-orientation to overcome the complexities and to enhance the extensibility. The framework can be used as a basis for modeling by various formalisms and to support model composition in a unified man...