Pseudo-labelling and Meta Reweighting Learning for Image Aesthetic Quality Assessment

• URL: http://arxiv.org/abs/2201.02714v1
• Date: Sat, 8 Jan 2022 00:43:01 GMT
• Title: Pseudo-labelling and Meta Reweighting Learning for Image Aesthetic Quality Assessment
• Authors: Xin Jin, Hao Lou, Huang Heng, Xiaodong Li, Shuai Cui, Xiaokun Zhang, Xiqiao Li
• Abstract summary: We propose a new aesthetic mixed dataset with classification and regression called AMD-CR. In the construction of the network structure, we construct an aesthetic adaptive block (AAB) structure that can adapt to any size of the input images. The experimental result shows that our method improves 0.1112 compared with the conventional methods in SROCC.
• Score: 11.35091532313198
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the tasks of image aesthetic quality evaluation, it is difficult to reach both the high score area and low score area due to the normal distribution of aesthetic datasets. To reduce the error in labeling and solve the problem of normal data distribution, we propose a new aesthetic mixed dataset with classification and regression called AMD-CR, and we train a meta reweighting network to reweight the loss of training data differently. In addition, we provide a training strategy acccording to different stages, based on pseudo labels of the binary classification task, and then we use it for aesthetic training acccording to different stages in classification and regression tasks. In the construction of the network structure, we construct an aesthetic adaptive block (AAB) structure that can adapt to any size of the input images. Besides, we also use the efficient channel attention (ECA) to strengthen the feature extracting ability of each task. The experimental result shows that our method improves 0.1112 compared with the conventional methods in SROCC. The method can also help to find best aesthetic path planning for unmanned aerial vehicles (UAV) and vehicles.

Related papers

• Enhancing pretraining efficiency for medical image segmentation via transferability metrics [0.0]
  In medical image segmentation tasks, the scarcity of labeled training data poses a significant challenge. We introduce a novel transferability metric, based on contrastive learning, that measures how robustly a pretrained model is able to represent the target data.
  arXiv  Detail & Related papers  (2024-10-24T12:11:52Z)
• Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning [42.14439854721613]
  We propose a prototypical network with a Bayesian learning-driven contrastive loss (BLCL) tailored specifically for class-incremental learning scenarios. Our approach dynamically adapts the balance between the cross-entropy and contrastive loss functions with a Bayesian learning technique.
  arXiv  Detail & Related papers  (2024-05-17T19:49:02Z)
• Improving GAN Training via Feature Space Shrinkage [69.98365478398593]
  We propose AdaptiveMix, which shrinks regions of training data in the image representation space of the discriminator. Considering it is intractable to directly bound feature space, we propose to construct hard samples and narrow down the feature distance between hard and easy samples. The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples.
  arXiv  Detail & Related papers  (2023-03-02T20:22:24Z)
• Imposing Consistency for Optical Flow Estimation [73.53204596544472]
  Imposing consistency through proxy tasks has been shown to enhance data-driven learning. This paper introduces novel and effective consistency strategies for optical flow estimation.
  arXiv  Detail & Related papers  (2022-04-14T22:58:30Z)
• Semi-weakly Supervised Contrastive Representation Learning for Retinal Fundus Images [0.2538209532048867]
  We propose a semi-weakly supervised contrastive learning framework for representation learning using semi-weakly annotated images. We empirically validate the transfer learning performance of SWCL on seven public retinal fundus datasets.
  arXiv  Detail & Related papers  (2021-08-04T15:50:09Z)
• Unifying Remote Sensing Image Retrieval and Classification with Robust Fine-tuning [3.6526118822907594]
  We aim at unifying remote sensing image retrieval and classification with a new large-scale training and testing dataset, SF300. We show that our framework systematically achieves a boost of retrieval and classification performance on nine different datasets compared to an ImageNet pretrained baseline.
  arXiv  Detail & Related papers  (2021-02-26T11:01:30Z)
• Mixed-Privacy Forgetting in Deep Networks [114.3840147070712]
  We show that the influence of a subset of the training samples can be removed from the weights of a network trained on large-scale image classification tasks. Inspired by real-world applications of forgetting techniques, we introduce a novel notion of forgetting in mixed-privacy setting. We show that our method allows forgetting without having to trade off the model accuracy.
  arXiv  Detail & Related papers  (2020-12-24T19:34:56Z)
• Sparse Signal Models for Data Augmentation in Deep Learning ATR [0.8999056386710496]
  We propose a data augmentation approach to incorporate domain knowledge and improve the generalization power of a data-intensive learning algorithm. We exploit the sparsity of the scattering centers in the spatial domain and the smoothly-varying structure of the scattering coefficients in the azimuthal domain to solve the ill-posed problem of over-parametrized model fitting.
  arXiv  Detail & Related papers  (2020-12-16T21:46:33Z)
• Background Splitting: Finding Rare Classes in a Sea of Background [55.03789745276442]
  We focus on the real-world problem of training accurate deep models for image classification of a small number of rare categories. In these scenarios, almost all images belong to the background category in the dataset (>95% of the dataset is background) We demonstrate that both standard fine-tuning approaches and state-of-the-art approaches for training on imbalanced datasets do not produce accurate deep models in the presence of this extreme imbalance.
  arXiv  Detail & Related papers  (2020-08-28T23:05:15Z)
• Multi-label Thoracic Disease Image Classification with Cross-Attention Networks [65.37531731899837]
  We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
  arXiv  Detail & Related papers  (2020-07-21T14:37:00Z)
• DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning [122.51237307910878]
  We develop methods for few-shot image classification from a new perspective of optimal matching between image regions. We employ the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations. To generate the important weights of elements in the formulation, we design a cross-reference mechanism.
  arXiv  Detail & Related papers  (2020-03-15T08:13:16Z)

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.