In the past few years, object detection has attracted a lot of attention in the context of human–robot collaboration and Industry 5.0 due to enormous quality improvements in deep learning technologies. In many applications, object detection models have to be able to quickly adapt to a changing environment, i.e., to learn new objects. A crucial but challenging prerequisite for this is the automatic generation of new training data which currently still limits the broad application of object detection methods in industrial manufacturing. In this work, we discuss how to adapt state-of-the-art object detection methods for the task of automatic bounding box annotation in a use case where the background is homogeneous and the object’s label is provided by a human. We compare an adapted version of Faster R-CNN and the Scaled-YOLOv4-p5 architecture and show that both can be trained to distinguish unknown objects from a complex but homogeneous background using only a small amount of training data. In contrast to most other state-of-the-art methods for bounding box labeling, our proposed method neither requires human verification, a predefined set of classes, nor a very large manually annotated dataset. Our method outperforms the state-of-the-art, transformer-based object discovery method LOST on our simple fruits dataset by large margins.
Gromit-MPX is an on-screen annotation tool that works with any Unix desktop environment under X11 as well as Wayland. - GitHub - bk138/gromit-mpx: Gromit-MPX is an on-screen annotation tool that works with any Unix desktop environment under X11 as well as Wayland.
@SafeVarargs
Is a cure for the warning: [unchecked] Possible heap pollution from parameterized vararg type Foo.
Is part of the method's contract, hence why the annotation has runtime retention.
Is a promise to the caller of the method that the method will not mess up the heap using the generic varargs argument.
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I've been thinking about the best approach to implement pure function verification in the Scala compiler. An approach similar to the one in D would fit a lot better than the one used in Haskell (which would break all existing code and cause some problems due to strict evaluation). A solution using annotations would be quite simple to implement:
Researchers at Google annotated English-language Web pages from the ClueWeb09 and ClueWeb12 corpora. The annotation process was automatic, and hence imperfect. However, the annotations are of generally high quality, as they strove for high precision (and, by necessity, lower recall). For each entity they recognized with high confidence, they provide the beginning and end byte offsets of the entity mention in the input text, its Freebase identifier (mid), and two confidence levels (computed differently, see below).
You might consider using this data in conjunction with the recently released Freebase annotations of several TREC query sets.
Concept search, full-text search and annotation structure search in one scaleable index: "Mímir is a multi-paradigm information management index and repository which can be used to index and search over text, annotations, semantic schemas (ontologies), and semantic meta-data (instance data). It allows queries that arbitrarily mix full-text, structural, linguistic and semantic queries and that can scale to gigabytes of text. A typical semantic annotation project deals with large quantities of data of different kinds. Mímir provides a framework for implementing indexing and search functionality across all these data type."
thumbtack collect, organize, share use thumbtack to collect a list of your favorite restaurants and share them with your friends plan a trip- collect information about places to stay and things to do research your next purchase- store, analyze and sift through your options in thumbtack take notes and share them with your team