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                            Library & Multimedia
 Visualizing Deep Similarity Networks
NCJ Number
              254594

Author(s)
          Abby Stylianou;
                        Richard Souvenir;
                        Robert Pless
Date Published

2019
Length
              9 pages
Annotation
              For convolutional neural network models that optimize an image embedding, this article proposes a method to highlight the regions of images that contribute most to pairwise similarity.
Abstract
              This work is a corollary to the visualization tools developed for classification networks, but applicable to the problem domains better suited to similarity learning. The visualization shows how similarity networks that are fine-tuned learn to focus on different features. This approach is generalized to embedding networks that use different pooling strategies and provides a simple mechanism to support image similarity searches on objects or sub-regions in the query image. (publisher abstract modified)
      
  
          Additional Details
Grant Number(s)
        2018-75-CX-0038


  
  
        
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        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)
                                Report (Study/Research)
                                Report (Grant Sponsored)
                                Program/Project Description


  
  
        
  Award
        An Object-Centric Approach for Image Analysis to Combat Human Trafficking
    
  
  
Date Published: January 1, 2019
    
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