The domain gap severely limits the transferability and scalability of object detectors trained in a specific when applied to novel one. Most existing works bridge by minimizing discrepancy category space aligning category-agnostic global features. Though great success, these methods model with prototypes within batch, yielding biased estimation domain-level distribution. Besides, alignment lead...

In this paper, we tackle the unsupervised domain adaptation (UDA) for semantic segmentation, which aims to segment unlabeled real data using labeled synthetic data. The main problem of UDA segmentation relies on reducing gap between image and image. To solve problem, focused separating information in an into content style. Here, only has cues style makes gap. Thus, precise separation leads effe...

In the current era, development in IOT has led to Web of Things advancements all fields life Sciences and medical Science. It’s helping people their lives but still due heterogeneity devices it is facing critical challenges. A smart novel semantic structure proposed for implementation M3 ontology Things, based on health domain triglyceride control system. Triglyceride related heart disease. The...

Deep neural networks are typically trained in a single shot for specific task and data distribution, but real world settings both the domain of application can change. The problem becomes even more challenging dense predictive tasks, such as semantic segmentation, furthermore most approaches tackle two problems separately. In this paper we introduce novel coarse-to-fine learning segmentation ar...

Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source with rich labeled data new target unlabeled data. Previous works focus on improving the adaptability of region-based detectors, e.g. , Faster-RCNN, through matching cross-domain instance...

Unsupervised domain adaptation (UDA) aims to adapt existing models of the source a new target with only unlabeled data. Most methods suffer from noticeable negative transfer resulting either error-prone discriminator network or unreasonable teacher model. Besides, local regional consistency in UDA has been largely neglected, and extracting global-level pattern information is not powerful enough...

Surgical procedures are conducted in highly complex operating rooms (OR), comprising different actors, devices, and interactions. To date, only medically trained human experts capable of understanding all the links interactions such a demanding environment. This paper aims to bring community one step closer automated, holistic semantic modeling OR domain. Towards this goal, for first time, we p...

This paper describes a semantic search tool based on our experience in using a new lexical domain ontology for aerospace integrated with an open source general purpose ontology to support aerospace engineers in the timely semantic retrieval of the knowledge. The semantic search module represents an integrated tool dedicated to the semantic search, extraction and classification of information an...