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Straight to Shapes: Real-time Detection of Encoded Shapes
Current object detection approaches predict bounding boxes, but these provide
little instance-specific information beyond location, scale and aspect ratio.
In this work, we propose to directly regress to objects' shapes in addition to
their bounding boxes and categories. It is crucial to find an appropriate shape
representation that is compact and decodable, and in which objects can be
compared for higher-order concepts such as view similarity, pose variation and
occlusion. To achieve this, we use a denoising convolutional auto-encoder to
establish an embedding space, and place the decoder after a fast end-to-end
network trained to regress directly to the encoded shape vectors. This yields
what to the best of our knowledge is the first real-time shape prediction
network, running at ~35 FPS on a high-end desktop. With higher-order shape
reasoning well-integrated into the network pipeline, the network shows the
useful practical quality of generalising to unseen categories similar to the
ones in the training set, something that most existing approaches fail to
handle.Comment: 16 pages including appendix; Published at CVPR 201
Recent advances in deep learning for object detection
Object detection is a fundamental visual recognition problem in computer
vision and has been widely studied in the past decades. Visual object detection
aims to find objects of certain target classes with precise localization in a
given image and assign each object instance a corresponding class label. Due to
the tremendous successes of deep learning based image classification, object
detection techniques using deep learning have been actively studied in recent
years. In this paper, we give a comprehensive survey of recent advances in
visual object detection with deep learning. By reviewing a large body of recent
related work in literature, we systematically analyze the existing object
detection frameworks and organize the survey into three major parts: (i)
detection components, (ii) learning strategies, and (iii) applications &
benchmarks. In the survey, we cover a variety of factors affecting the
detection performance in detail, such as detector architectures, feature
learning, proposal generation, sampling strategies, etc. Finally, we discuss
several future directions to facilitate and spur future research for visual
object detection with deep learning. Keywords: Object Detection, Deep Learning,
Deep Convolutional Neural Network
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