PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks

• URL: http://arxiv.org/abs/2005.02154v2
• Date: Wed, 5 Aug 2020 13:12:10 GMT
• Title: PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks
• Authors: Benyi Hu, Ren-Jie Song, Xiu-Shen Wei, Yazhou Yao, Xian-Sheng Hua, and Yuehu Liu
• Abstract summary: PyRetri is an open source library for deep learning based unsupervised image retrieval. It encapsulates the retrieval process in several stages and provides functionality that covers various prominent methods for each stage.
• Score: 49.35908338404728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite significant progress of applying deep learning methods to the field of content-based image retrieval, there has not been a software library that covers these methods in a unified manner. In order to fill this gap, we introduce PyRetri, an open source library for deep learning based unsupervised image retrieval. The library encapsulates the retrieval process in several stages and provides functionality that covers various prominent methods for each stage. The idea underlying its design is to provide a unified platform for deep learning based image retrieval research, with high usability and extensibility. To the best of our knowledge, this is the first open-source library for unsupervised image retrieval by deep learning.

Related papers

• Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections [0.0]
  This paper presents a comparative study of near-duplicate image detection techniques in a real-world use case scenario. We propose a transductive learning approach that leverages state-of-the-art deep learning architectures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs) The results show that the proposed approach outperforms the baseline methods in the task of near-duplicate image detection in the UKBench and an in-house private dataset.
  arXiv  Detail & Related papers  (2024-10-25T09:56:15Z)
• Visual Navigation of Digital Libraries: Retrieval and Classification of Images in the National Library of Norway's Digitised Book Collection [0.3277163122167433]
  We present a proof-of-concept image search application for exploring images in the National Library of Norway's pre-1900 books. We compare Vision Transformer (ViT), Contrastive Language-Image Pre-training (CLIP), and Sigmoid loss for Language-Image Pre-training (SigLIP) embeddings for image retrieval and classification.
  arXiv  Detail & Related papers  (2024-10-19T04:20:23Z)
• LibFewShot: A Comprehensive Library for Few-shot Learning [78.58842209282724]
  Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly show that many generic techniques or tricks, such as data augmentation, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few-shot learning method. We propose a comprehensive library for few-shot learning (LibFewShot) by re-implementing seventeen state-of-the-art few-shot learning methods in a unified framework with the same single intrinsic in PyTorch.
  arXiv  Detail & Related papers  (2021-09-10T14:12:37Z)
• Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning [83.02597612195966]
  solo-learn is a library of self-supervised methods for visual representation learning. Implemented in Python, using Pytorch and Pytorch lightning, the library fits both research and industry needs.
  arXiv  Detail & Related papers  (2021-08-03T22:19:55Z)
• AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation [3.6790362352712873]
  We propose AugNet, a new deep learning training paradigm to learn image features from a collection of unlabeled pictures. Our experiments demonstrate that the method is able to represent the image in low dimensional space. Unlike many deep-learning-based image retrieval algorithms, our approach does not require access to external annotated datasets.
  arXiv  Detail & Related papers  (2021-06-11T09:02:30Z)
• A Decade Survey of Content Based Image Retrieval using Deep Learning [13.778851745408133]
  This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval. The similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. Deep learning has emerged as a dominating alternative of hand-designed feature engineering from a decade.
  arXiv  Detail & Related papers  (2020-11-23T02:12:30Z)
• NAS-DIP: Learning Deep Image Prior with Neural Architecture Search [65.79109790446257]
  Recent work has shown that the structure of deep convolutional neural networks can be used as a structured image prior. We propose to search for neural architectures that capture stronger image priors. We search for an improved network by leveraging an existing neural architecture search algorithm.
  arXiv  Detail & Related papers  (2020-08-26T17:59:36Z)
• Distilling Localization for Self-Supervised Representation Learning [82.79808902674282]
  Contrastive learning has revolutionized unsupervised representation learning. Current contrastive models are ineffective at localizing the foreground object. We propose a data-driven approach for learning in variance to backgrounds.
  arXiv  Detail & Related papers  (2020-04-14T16:29:42Z)
• TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning [68.8204255655161]
  We present TorchIO, an open-source Python library to enable efficient loading, preprocessing, augmentation and patch-based sampling of medical images for deep learning. TorchIO follows the style of PyTorch and integrates standard medical image processing libraries to efficiently process images during training of neural networks. It includes a command-line interface which allows users to apply transforms to image files without using Python.
  arXiv  Detail & Related papers  (2020-03-09T13:36:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.