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Representation (systemics)

About: Representation (systemics) is a research topic. Over the lifetime, 33821 publications have been published within this topic receiving 475461 citations.


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TL;DR: More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics as mentioned in this paper.
Abstract: Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

90 citations

Posted Content
22 Jun 2014
TL;DR: This work proposes to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network, and naturally supports object recognition from 2.5D depth map and also view planning for object recognition.
Abstract: 3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representations automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet -- a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.

90 citations

Book ChapterDOI
17 Sep 1996
TL;DR: Insight is given into the diverse alternatives for the representation of transitive relations such as part-whole relations, family relations or partial orders in general in terminological knowledge representation systems.
Abstract: Motivated by applications that demand for the adequate representation of part-whole relations, different possibilities of representing transitive relations in terminological knowledge representation systems axe investigated. A well-known concept language, ALC, is extended by three different kinds of transitive roles. It turns out that these extensions differ largely in expressiveness and computational complexity, hence this investigation gives insight into the diverse alternatives for the representation of transitive relations such as part-whole relations, family relations or partial orders in general.

90 citations

01 Jan 2005
TL;DR: A general probabilistic setting that formalizes the notion of textual entailment and a concrete model for lexical entailment based on web co-occurrence statistics in a bag of words representation are proposed.
Abstract: This paper proposes a general probabilistic setting that formalizes the notion of textual entailment. In addition we describe a concrete model for lexical entailment based on web co-occurrence statistics in a bag of words representation.

90 citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202225
20211,580
20201,876
20191,935
20181,792
20171,391