Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916156875472896 |
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| author | Kumar, Chandan Herrera-Gerena, Jansel Just, John Darr, Matthew Jannesari, Ali |
| author_facet | Kumar, Chandan Herrera-Gerena, Jansel Just, John Darr, Matthew Jannesari, Ali |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07231 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning Kumar, Chandan Herrera-Gerena, Jansel Just, John Darr, Matthew Jannesari, Ali Computer Vision and Pattern Recognition 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. |
| title | Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.07231 |