With the success of Transformers in natural language processing, object detection with Transformers (DETR) has attracted widespread attentions. In previous Transformer-based 2D detectors, the object queries are a set ...
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With the success of Transformers in natural language processing, object detection with Transformers (DETR) has attracted widespread attentions. In previous Transformer-based 2D detectors, the object queries are a set of learning embeddings. However, it is very hard to apply these detectors to the 3D domain due to the lack of explicit physical meanings and position priors of learned object queries. In this paper, we introduce the concept of anchors and propose a novel query design based on anchor points. In our query design, we use the foreground points as the anchor points and encode these anchor points as the object queries. Consequently, each object query has an explicit physical meaning and only focus on its nearby object. Additionally, we also propose an instance-aware sampling strategy to select a small set of representation foreground points from the scene point cloud. Extensive experiments on several large-scale 3D object detection datasets demonstrate that the proposed AnchorPoint detector achieves promising accuracy and efficiency. In particularly, AnchorPoint achieves an average precision (AP) of 83.21 at 61 frame-per-second (FPS) on the moderate level of the KITTI-DET Car subset. Moreover, we model each object as its corresponding anchor point, and extend the AnchorPoint model to 3D multi-object tracking by adding an extra tracking head. We show that our method achieves comparable performance to existing state-of-the-art methods on the KITTI-MOT dataset.
Background Sharing sensitive data across organizational boundaries is often significantly limited by legal and ethical restrictions. Regulations such as the EU General Data Protection Rules (GDPR) impose strict requir...
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Background Sharing sensitive data across organizational boundaries is often significantly limited by legal and ethical restrictions. Regulations such as the EU General Data Protection Rules (GDPR) impose strict requirements concerning the protection of personal and privacy sensitive data. Therefore new approaches, such as the Personal Health Train initiative, are emerging to utilize data right in their original repositories, circumventing the need to transfer data. Results Circumventing limitations of previous systems, this paper proposes a configurable and automated schema extraction and publishing approach, which enables ad-hoc SPARQL query formulation against RDF triple stores without requiring direct access to the private data. The approach is compatible with existing Semantic Web-based technologies and allows for the subsequent execution of such queries in a safe setting under the data provider's control. Evaluation with four distinct datasets shows that a configurable amount of concise and task-relevant schema, closely describing the structure of the underlying data, was derived, enabling the schema introspection-assisted authoring of SPARQL queries. Conclusions Automatically extracting and publishing data schema can enable the introspection-assisted creation of data selection and integration queries. In conjunction with the presented system architecture, this approach can enable reuse of data from private repositories and in settings where agreeing upon a shared schema and encoding a priori is infeasible. As such, it could provide an important step towards reuse of data from previously inaccessible sources and thus towards the proliferation of data-driven methods in the biomedical domain.
Relational algebra provides a theoretical foundation for how modern database management systems optimize and execute queries. Its main concepts are based on set theory and first order logic, which can be challenging f...
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ISBN:
(纸本)9781450326056
Relational algebra provides a theoretical foundation for how modern database management systems optimize and execute queries. Its main concepts are based on set theory and first order logic, which can be challenging for students to learn due to their abstract nature. This paper presents Bags, a new type of visual programming environment (inspired by Snap!) for the teaching of relational operations and data analysis. Students formulate algebraic queries by snapping together graphical blocks that represent data sets and relational operators, resulting in an interactive visualization of the underlying concepts. The outcomes of this work will not only enhance university-level database courses, but also provide an engaging computational thinking resource for K-12 teachers in content areas outside of science and engineering.
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