Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions

• URL: http://arxiv.org/abs/2306.07383v1
• Date: Mon, 12 Jun 2023 19:17:44 GMT
• Title: Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions
• Authors: MohammadHossein Givkashi, MohammadReza Naderi, Nader Karimi, Shahram Shirani, Shadrokh Samavi
• Abstract summary: Image aims to alter the size of the image with attention to the contents. Labeled datasets are unavailable for training deep learning models in the image tasks. Regular convolutional neural networks cannot generate images of different sizes in inference time.
• Score: 11.031841470875571
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Image retargeting aims to alter the size of the image with attention to the contents. One of the main obstacles to training deep learning models for image retargeting is the need for a vast labeled dataset. Labeled datasets are unavailable for training deep learning models in the image retargeting tasks. As a result, we present a new supervised approach for training deep learning models. We use the original images as ground truth and create inputs for the model by resizing and cropping the original images. A second challenge is generating different image sizes in inference time. However, regular convolutional neural networks cannot generate images of different sizes than the input image. To address this issue, we introduced a new method for supervised learning. In our approach, a mask is generated to show the desired size and location of the object. Then the mask and the input image are fed to the network. Comparing image retargeting methods and our proposed method demonstrates the model's ability to produce high-quality retargeted images. Afterward, we compute the image quality assessment score for each output image based on different techniques and illustrate the effectiveness of our approach.

Related papers

• Data Attribution for Text-to-Image Models by Unlearning Synthesized Images [71.23012718682634]
  The goal of data attribution for text-to-image models is to identify the training images that most influence the generation of a new image. We propose a new approach that efficiently identifies highly-influential images.
  arXiv  Detail & Related papers  (2024-06-13T17:59:44Z)
• Interactive Image Selection and Training for Brain Tumor Segmentation Network [42.62139206176152]
  We employ an interactive method for image selection and training based on Feature Learning from Image Markers (FLIM) The results demonstrated that with our methodology, we could choose a small set of images to train the encoder of a U-shaped network, obtaining performance equal to manual selection and even surpassing the same U-shaped network trained with backpropagation and all training images.
  arXiv  Detail & Related papers  (2024-06-05T13:03:06Z)
• Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation [78.13793505707952]
  Existing autoregressive models follow the two-stage generation paradigm that first learns a codebook in the latent space for image reconstruction and then completes the image generation autoregressively based on the learned codebook. We propose a novel two-stage framework, which consists of Masked Quantization VAE (MQ-VAE) Stack model from modeling redundancy.
  arXiv  Detail & Related papers  (2023-05-23T02:15:53Z)
• Improving Masked Autoencoders by Learning Where to Mask [65.89510231743692]
  Masked image modeling is a promising self-supervised learning method for visual data. We present AutoMAE, a framework that uses Gumbel-Softmax to interlink an adversarially-trained mask generator and a mask-guided image modeling process. In our experiments, AutoMAE is shown to provide effective pretraining models on standard self-supervised benchmarks and downstream tasks.
  arXiv  Detail & Related papers  (2023-03-12T05:28:55Z)
• Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification [3.9310727060473476]
  This paper proposes a new genetic programming-based evolutionary deep learning approach to data-efficient image classification. The new approach can automatically evolve variable-length models using many important operators from both image and classification domains. A flexible multi-layer representation enables the new approach to automatically construct shallow or deep models/trees for different tasks.
  arXiv  Detail & Related papers  (2022-09-27T08:10:16Z)
• The Devil is in the Frequency: Geminated Gestalt Autoencoder for Self-Supervised Visual Pre-Training [13.087987450384036]
  We present a new Masked Image Modeling (MIM), termed Geminated Autoencoder (Ge$2$-AE) for visual pre-training. Specifically, we equip our model with geminated decoders in charge of reconstructing image contents from both pixel and frequency space.
  arXiv  Detail & Related papers  (2022-04-18T09:22:55Z)
• 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)
• Rectifying the Shortcut Learning of Background: Shared Object Concentration for Few-Shot Image Recognition [101.59989523028264]
  Few-Shot image classification aims to utilize pretrained knowledge learned from a large-scale dataset to tackle a series of downstream classification tasks. We propose COSOC, a novel Few-Shot Learning framework, to automatically figure out foreground objects at both pretraining and evaluation stage.
  arXiv  Detail & Related papers  (2021-07-16T07:46:41Z)
• 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)

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.