Zero-sample target detection system and learnable semantic and fixed semantic fusion method

A target detection and sample technology, applied in the field of machine learning, can solve the problems of neural network training difficulties, weak identification ability, etc., and achieve the effect of improving accuracy

Active Publication Date: 2020-12-04
FUDAN UNIV
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Problems solved by technology

[0009] In order to improve the accuracy of image target recognition using the zero-shot learning method, this application provides a zero-shot target detection system and a fusion method of learnable semantics and fixed semantics, which combines learnable semantic features and fixed semantic vectors for zero-shot target detection The algorith

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  • Zero-sample target detection system and learnable semantic and fixed semantic fusion method
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  • Zero-sample target detection system and learnable semantic and fixed semantic fusion method

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Embodiment Construction

[0056] The application will be described in further detail below in conjunction with all the accompanying drawings.

[0057] The specific description of the zero-sample target detection problem is as follows: Assume that n tr visible classes and n ts Objects in unseen classes are detected, and the seen and unseen class spaces are disjoint. On the visible class space, given n tr A training set D labeled with target location and category information tr ={(b k , I k ,Y k ,a k ),k=1...n tr}, where b k is the kth label box, I k , Y k 、a k are the image, category label, and semantic attribute vector corresponding to the kth annotation box, respectively. while b k With a 4-tuple (x k ,y k ,w k ,h k ) to represent, where the first two elements x k and y k Indicates the coordinates of the upper left corner of the kth label box, and the last two values ​​w k and h k are the width and height of the kth annotation box, respectively. Given a fixed category semantic ma...

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Abstract

The invention discloses a zero-sample target detection system and a learnable semantic and fixed semantic fusion method, a zero-sample learning mechanism is introduced into a deep target detection framework, a set of zero-sample target detection system LATNet with strong discrimination capability is established, and an end-to-end zero-sample target detection task is realized through the LATNet. Alearnable semantic feature and fixed semantic feature combined method is used, so that when a network is trained in a source domain, word vector information of a category can be fully utilized, end-to-end learning can also be utilized, a category prototype with better identification capability is discovered, and the best detection accuracy is obtained. The system is simple in framework, convenientto use, high in expandability and high in interpretability, and the results of the two tasks of zero sample detection and generalized zero sample detection of the two mainstream visual attribute datasets exceed those of an existing method. And the support of a basic framework and a method is provided for the target detection technology in the military and industrial application fields.

Description

technical field [0001] The present application relates to the technical field of machine learning, in particular to a zero-sample object detection system and a fusion method of learnable semantics and fixed semantics. Background technique [0002] Target detection technology is a basic task in computer vision tasks, which aims to locate and classify target category objects from images. Target detection technology has a wide range of applications, and it provides basic support for some downstream tasks, such as instance segmentation, scene understanding, pose estimation and other tasks. Existing deep object detection models have achieved good accuracy in some categories, but rely heavily on large-scale calibrated datasets. However, in real scenarios, we are faced with problems such as unbalanced distribution of data samples and unsupervised samples. Therefore, how to make full use of the data in social media when the sample size is insufficient or even zero samples, and the ...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V2201/07G06N3/045G06F18/2414G06F18/254G06F18/2415G06F18/253
Inventor 周水庚王康张路赵佳佳
Owner FUDAN UNIV
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