Curiously Effective Features for Image Quality Prediction

• URL: http://arxiv.org/abs/2106.05946v1
• Date: Thu, 10 Jun 2021 17:44:04 GMT
• Title: Curiously Effective Features for Image Quality Prediction
• Authors: S\"oren Becker, Thomas Wiegand, Sebastian Bosse
• Abstract summary: We show that besides the quality of feature extractors also their quantity plays a crucial role. We analyze this curious result and show that besides the quality of feature extractors also their quantity plays a crucial role.
• Score: 8.55016170630223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The performance of visual quality prediction models is commonly assumed to be closely tied to their ability to capture perceptually relevant image aspects. Models are thus either based on sophisticated feature extractors carefully designed from extensive domain knowledge or optimized through feature learning. In contrast to this, we find feature extractors constructed from random noise to be sufficient to learn a linear regression model whose quality predictions reach high correlations with human visual quality ratings, on par with a model with learned features. We analyze this curious result and show that besides the quality of feature extractors also their quantity plays a crucial role - with top performances only being achieved in highly overparameterized models.

Related papers

• Opinion-Unaware Blind Image Quality Assessment using Multi-Scale Deep Feature Statistics [54.08757792080732]
  We propose integrating deep features from pre-trained visual models with a statistical analysis model to achieve opinion-unaware BIQA (OU-BIQA) Our proposed model exhibits superior consistency with human visual perception compared to state-of-the-art BIQA models.
  arXiv  Detail & Related papers  (2024-05-29T06:09:34Z)
• The Importance of Model Inspection for Better Understanding Performance Characteristics of Graph Neural Networks [15.569758991934934]
  We investigate the effect of modelling choices on the feature learning characteristics of graph neural networks applied to a brain shape classification task. We find substantial differences in the feature embeddings at different layers of the models.
  arXiv  Detail & Related papers  (2024-05-02T13:26:18Z)
• Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval [65.43522019468976]
  We propose attribute-aware hashing networks with self-consistency for generating attribute-aware hash codes. We develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors. Our models are equipped with a feature decorrelation constraint upon these attribute vectors to strengthen their representative abilities.
  arXiv  Detail & Related papers  (2023-11-21T08:20:38Z)
• Study of Distractors in Neural Models of Code [4.043200001974071]
  Finding important features that contribute to the prediction of neural models is an active area of research in explainable AI. In this work, we present an inverse perspective of distractor features: features that cast doubt about the prediction by affecting the model's confidence in its prediction. Our experiments across various tasks, models, and datasets of code reveal that the removal of tokens can have a significant impact on the confidence of models in their predictions.
  arXiv  Detail & Related papers  (2023-03-03T06:54:01Z)
• DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator [53.57431705309919]
  ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models. We develop a novel full-reference IQA (FR-IQA) model based exclusively on pre-trained DNN features. We conduct comprehensive experiments to demonstrate the superiority of the proposed quality model on five standard IQA datasets.
  arXiv  Detail & Related papers  (2022-11-09T14:57:27Z)
• FUNQUE: Fusion of Unified Quality Evaluators [42.41484412777326]
  Fusion-based quality assessment has emerged as a powerful method for developing high-performance quality models. We propose FUNQUE, a quality model that fuses unified quality evaluators.
  arXiv  Detail & Related papers  (2022-02-23T00:21:43Z)
• A Relational Model for One-Shot Classification [80.77724423309184]
  We show that a deep learning model with built-in inductive bias can bring benefits to sample-efficient learning, without relying on extensive data augmentation. The proposed one-shot classification model performs relational matching of a pair of inputs in the form of local and pairwise attention.
  arXiv  Detail & Related papers  (2021-11-08T07:53:12Z)
• Enhancing Model Robustness and Fairness with Causality: A Regularization Approach [15.981724441808147]
  Recent work has raised concerns on the risk of spurious correlations and unintended biases in machine learning models. We propose a simple and intuitive regularization approach to integrate causal knowledge during model training. We build a predictive model that relies more on causal features and less on non-causal features.
  arXiv  Detail & Related papers  (2021-10-03T02:49:33Z)
• What Image Features Boost Housing Market Predictions? [81.32205133298254]
  We propose a set of techniques for the extraction of visual features for efficient numerical inclusion in predictive algorithms. We discuss techniques such as Shannon's entropy, calculating the center of gravity, employing image segmentation, and using Convolutional Neural Networks. The set of 40 image features selected here carries a significant amount of predictive power and outperforms some of the strongest metadata predictors.
  arXiv  Detail & Related papers  (2021-07-15T06:32:10Z)
• Characterizing Fairness Over the Set of Good Models Under Selective Labels [69.64662540443162]
  We develop a framework for characterizing predictive fairness properties over the set of models that deliver similar overall performance. We provide tractable algorithms to compute the range of attainable group-level predictive disparities. We extend our framework to address the empirically relevant challenge of selectively labelled data.
  arXiv  Detail & Related papers  (2021-01-02T02:11:37Z)
• Better Model Selection with a new Definition of Feature Importance [8.914907178577476]
  Feature importance aims at measuring how crucial each input feature is for model prediction. In this paper, we propose a new tree-model explanation approach for model selection.
  arXiv  Detail & Related papers  (2020-09-16T14:32:22Z)

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.