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Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning

Chandan Kumar, Jansel Herrera-Gerena, John Just, Matthew Darr, Ali Jannesari

TL;DR

A novel approach for object lookup that leverages the power of contrastive learning to enhance the efficiency and effectiveness of retrieval systems and presents an elegant and innovative methodology that integrates deep learning principles and contrastive learning to tackle the challenges of object search.

Abstract

The rapid proliferation of digital content and the ever-growing need for precise object recognition and segmentation have driven the advancement of cutting-edge techniques in the field of object classification and segmentation. This paper introduces "Learn and Search", a novel approach for object lookup that leverages the power of contrastive learning to enhance the efficiency and effectiveness of retrieval systems. In this study, we present an elegant and innovative methodology that integrates deep learning principles and contrastive learning to tackle the challenges of object search. Our extensive experimentation reveals compelling results, with "Learn and Search" achieving superior Similarity Grid Accuracy, showcasing its efficacy in discerning regions of utmost similarity within an image relative to a cropped image. The seamless fusion of deep learning and contrastive learning to address the intricacies of object identification not only promises transformative applications in image recognition, recommendation systems, and content tagging but also revolutionizes content-based search and retrieval. The amalgamation of these techniques, as exemplified by "Learn and Search," represents a significant stride in the ongoing evolution of methodologies in the dynamic realm of object classification and segmentation.

Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning

TL;DR

A novel approach for object lookup that leverages the power of contrastive learning to enhance the efficiency and effectiveness of retrieval systems and presents an elegant and innovative methodology that integrates deep learning principles and contrastive learning to tackle the challenges of object search.

Abstract

The rapid proliferation of digital content and the ever-growing need for precise object recognition and segmentation have driven the advancement of cutting-edge techniques in the field of object classification and segmentation. This paper introduces "Learn and Search", a novel approach for object lookup that leverages the power of contrastive learning to enhance the efficiency and effectiveness of retrieval systems. In this study, we present an elegant and innovative methodology that integrates deep learning principles and contrastive learning to tackle the challenges of object search. Our extensive experimentation reveals compelling results, with "Learn and Search" achieving superior Similarity Grid Accuracy, showcasing its efficacy in discerning regions of utmost similarity within an image relative to a cropped image. The seamless fusion of deep learning and contrastive learning to address the intricacies of object identification not only promises transformative applications in image recognition, recommendation systems, and content tagging but also revolutionizes content-based search and retrieval. The amalgamation of these techniques, as exemplified by "Learn and Search," represents a significant stride in the ongoing evolution of methodologies in the dynamic realm of object classification and segmentation.
Paper Structure (10 sections, 2 equations, 4 figures, 2 tables)

This paper contains 10 sections, 2 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Contributions
  4. Our Method
  5. Augmentations
  6. Experiments
  7. Results
  8. Accuracy for "Learn"
  9. Accuracy for "Search"
  10. Conclusion

Figures (4)

  • Figure 1: Flowchart for our methodology. $x$ is input image; $x_i$ is the random crop of original image($x$) and $x_j$ is the augmented image; $r_i$ is the representation from resnet; $z_i$ and $z_j$ and are the embeddings from $x_i$ and $x_j$ respectively; $z_a$ is the anchor embedding
  • Figure 2: We have used augmentations extensively in our experiments. In this visualization, we present all the augmentations we have used in the experiments along with all the interpolations.
  • Figure 3: Performance of different models
  • Figure 4: This figure shows a grid of images gathered after selecting a crop within the dataset and searching the top10 similar images. The selected crop is passed to the RetinaNet to produce a representation and the highest similarity images are process on a batch to produce the FPN outputs used to compare and execute the selection.