Identical Image Retrieval using Deep Learning

• URL: http://arxiv.org/abs/2205.04883v1
• Date: Tue, 10 May 2022 13:34:41 GMT
• Title: Identical Image Retrieval using Deep Learning
• Authors: Sayan Nath, Nikhil Nayak
• Abstract summary: We are using the BigTransfer Model, which is a state-of-art model itself. We extract the key features and train on the K-Nearest Neighbor model to obtain the nearest neighbor. The application of our model is to find similar images, which are hard to achieve through text queries within a low inference time.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, we know that the interaction with images has increased. Image similarity involves fetching similar-looking images abiding by a given reference image. The target is to find out whether the image searched as a query can result in similar pictures. We are using the BigTransfer Model, which is a state-of-art model itself. BigTransfer(BiT) is essentially a ResNet but pre-trained on a larger dataset like ImageNet and ImageNet-21k with additional modifications. Using the fine-tuned pre-trained Convolution Neural Network Model, we extract the key features and train on the K-Nearest Neighbor model to obtain the nearest neighbor. The application of our model is to find similar images, which are hard to achieve through text queries within a low inference time. We analyse the benchmark of our model based on this application.

Related papers

• ImageNot: A contrast with ImageNet preserves model rankings [16.169858780154893]
  We introduce ImageNot, a dataset designed to match the scale of ImageNet while differing drastically in other aspects. Key model architectures developed for ImageNet over the years rank identically when trained and evaluated on ImageNot to how they rank on ImageNet.
  arXiv  Detail & Related papers  (2024-04-02T17:13:04Z)
• FreeSeg-Diff: Training-Free Open-Vocabulary Segmentation with Diffusion Models [56.71672127740099]
  We focus on the task of image segmentation, which is traditionally solved by training models on closed-vocabulary datasets. We leverage different and relatively small-sized, open-source foundation models for zero-shot open-vocabulary segmentation. Our approach (dubbed FreeSeg-Diff), which does not rely on any training, outperforms many training-based approaches on both Pascal VOC and COCO datasets.
  arXiv  Detail & Related papers  (2024-03-29T10:38:25Z)
• ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object [78.58860252442045]
  We introduce generative model as a data source for hard images that benchmark deep models' robustness. We are able to generate images with more diversified backgrounds, textures, and materials than any prior work, where we term this benchmark as ImageNet-D. Our work suggests that diffusion models can be an effective source to test vision models.
  arXiv  Detail & Related papers  (2024-03-27T17:23:39Z)
• Image Similarity using An Ensemble of Context-Sensitive Models [2.9490616593440317]
  We present a more intuitive approach to build and compare image similarity models based on labelled data. We address the challenges of sparse sampling in the image space (R, A, B) and biases in the models trained with context-based data. Our testing results show that the ensemble model constructed performs 5% better than the best individual context-sensitive models.
  arXiv  Detail & Related papers  (2024-01-15T20:23:05Z)
• ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing [45.14977000707886]
  Higher accuracy on ImageNet usually leads to better robustness against different corruptions. We create a toolkit for object editing with controls of backgrounds, sizes, positions, and directions. We evaluate the performance of current deep learning models, including both convolutional neural networks and vision transformers.
  arXiv  Detail & Related papers  (2023-03-30T02:02:32Z)
• Fake it till you make it: Learning transferable representations from synthetic ImageNet clones [30.264601433216246]
  We show that ImageNet clones can close a large part of the gap between models produced by synthetic images and models trained with real images. We show that models trained on synthetic images exhibit strong generalization properties and perform on par with models trained on real data for transfer.
  arXiv  Detail & Related papers  (2022-12-16T11:44:01Z)
• Meta Internal Learning [88.68276505511922]
  Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image. We propose a meta-learning approach that enables training over a collection of images, in order to model the internal statistics of the sample image more effectively. Our results show that the models obtained are as suitable as single-image GANs for many common image applications.
  arXiv  Detail & Related papers  (2021-10-06T16:27:38Z)
• Rethinking Natural Adversarial Examples for Classification Models [43.87819913022369]
  ImageNet-A is a famous dataset of natural adversarial examples. We validated the hypothesis by reducing the background influence in ImageNet-A examples with object detection techniques. Experiments showed that the object detection models with various classification models as backbones obtained much higher accuracy than their corresponding classification models.
  arXiv  Detail & Related papers  (2021-02-23T14:46:48Z)
• Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation [49.90178055521207]
  This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation. We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths. In particular, we devise a graph neural network (GNN) for group-wise semantic mining, wherein input images are represented as graph nodes.
  arXiv  Detail & Related papers  (2020-12-09T12:40:13Z)
• Shape-Texture Debiased Neural Network Training [50.6178024087048]
  Convolutional Neural Networks are often biased towards either texture or shape, depending on the training dataset. We develop an algorithm for shape-texture debiased learning. Experiments show that our method successfully improves model performance on several image recognition benchmarks.
  arXiv  Detail & Related papers  (2020-10-12T19:16:12Z)
• CRNet: Cross-Reference Networks for Few-Shot Segmentation [59.85183776573642]
  Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images. With a cross-reference mechanism, our network can better find the co-occurrent objects in the two images. Experiments on the PASCAL VOC 2012 dataset show that our network achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-03-24T04:55:43Z)

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.