Convolutional Ordinal Regression Forest for Image Ordinal Estimation
- URL: http://arxiv.org/abs/2008.03077v2
- Date: Wed, 27 Jan 2021 05:20:02 GMT
- Title: Convolutional Ordinal Regression Forest for Image Ordinal Estimation
- Authors: Haiping Zhu, Hongming Shan, Yuheng Zhang, Lingfu Che, Xiaoyang Xu,
Junping Zhang, Jianbo Shi, Fei-Yue Wang
- Abstract summary: We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation.
The proposed CORF integrates ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships.
The effectiveness of the proposed CORF is verified on two image ordinal estimation tasks, showing significant improvements and better stability over the state-of-the-art ordinal regression methods.
- Score: 52.67784321853814
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Image ordinal estimation is to predict the ordinal label of a given image,
which can be categorized as an ordinal regression problem. Recent methods
formulate an ordinal regression problem as a series of binary classification
problems. Such methods cannot ensure that the global ordinal relationship is
preserved since the relationships among different binary classifiers are
neglected. We propose a novel ordinal regression approach, termed Convolutional
Ordinal Regression Forest or CORF, for image ordinal estimation, which can
integrate ordinal regression and differentiable decision trees with a
convolutional neural network for obtaining precise and stable global ordinal
relationships. The advantages of the proposed CORF are twofold. First, instead
of learning a series of binary classifiers \emph{independently}, the proposed
method aims at learning an ordinal distribution for ordinal regression by
optimizing those binary classifiers \emph{simultaneously}. Second, the
differentiable decision trees in the proposed CORF can be trained together with
the ordinal distribution in an end-to-end manner. The effectiveness of the
proposed CORF is verified on two image ordinal estimation tasks, i.e. facial
age estimation and image aesthetic assessment, showing significant improvements
and better stability over the state-of-the-art ordinal regression methods.
Related papers
- Generating Unbiased Pseudo-labels via a Theoretically Guaranteed
Chebyshev Constraint to Unify Semi-supervised Classification and Regression [57.17120203327993]
threshold-to-pseudo label process (T2L) in classification uses confidence to determine the quality of label.
In nature, regression also requires unbiased methods to generate high-quality labels.
We propose a theoretically guaranteed constraint for generating unbiased labels based on Chebyshev's inequality.
arXiv Detail & Related papers (2023-11-03T08:39:35Z) - Deep Imbalanced Regression via Hierarchical Classification Adjustment [50.19438850112964]
Regression tasks in computer vision are often formulated into classification by quantizing the target space into classes.
The majority of training samples lie in a head range of target values, while a minority of samples span a usually larger tail range.
We propose to construct hierarchical classifiers for solving imbalanced regression tasks.
Our novel hierarchical classification adjustment (HCA) for imbalanced regression shows superior results on three diverse tasks.
arXiv Detail & Related papers (2023-10-26T04:54:39Z) - Distribution-Free Inference for the Regression Function of Binary
Classification [0.0]
The paper presents a resampling framework to construct exact, distribution-free and non-asymptotically guaranteed confidence regions for the true regression function for any user-chosen confidence level.
It is proved that the constructed confidence regions are strongly consistent, that is, any false model is excluded in the long run with probability one.
arXiv Detail & Related papers (2023-08-03T15:52:27Z) - Ord2Seq: Regarding Ordinal Regression as Label Sequence Prediction [13.844821175622794]
We propose a simple sequence prediction framework for ordinal regression called Ord2Seq.
We decompose an ordinal regression task into a series of binary classification steps, so as to subtly distinguish adjacent categories.
Our new approach exceeds state-of-the-art performances in four different scenarios.
arXiv Detail & Related papers (2023-07-18T06:44:20Z) - CORE: Learning Consistent Ordinal REpresentations for Image Ordinal
Estimation [35.39143939072549]
This paper proposes learning intrinsic Consistent Ordinal REpresentations (CORE) from ordinal relations residing in groundtruth labels.
CORE can accurately construct an ordinal latent space and significantly enhance existing deep ordinal regression methods to achieve better results.
arXiv Detail & Related papers (2023-01-15T15:42:26Z) - Training and Inference on Any-Order Autoregressive Models the Right Way [97.39464776373902]
A family of Any-Order Autoregressive Models (AO-ARMs) has shown breakthrough performance in arbitrary conditional tasks.
We identify significant improvements to be made to previous formulations of AO-ARMs.
Our method leads to improved performance with no compromises on tractability.
arXiv Detail & Related papers (2022-05-26T18:00:02Z) - Meta Ordinal Regression Forest for Medical Image Classification with
Ordinal Labels [37.121792169424744]
We propose a novel meta ordinal regression forest (MORF) method for medical image classification with ordinal labels.
MORF learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning framework.
Experimental results on two medical image classification datasets with ordinal labels demonstrate the superior performances of our MORF method over existing state-of-the-art methods.
arXiv Detail & Related papers (2022-03-15T08:43:57Z) - Meta Ordinal Regression Forest For Learning with Unsure Lung Nodules [18.597354524446487]
An unsure data model (UDM) was proposed to incorporate those unsure nodules by formulating this problem as an ordinal regression.
This paper proposes a meta ordinal regression forest (MORF) which improves upon the state-of-the-art ordinal regression method.
Experimental results on the LIDC-IDRI dataset demonstrate superior performance over existing methods.
arXiv Detail & Related papers (2020-12-07T06:59:43Z) - Pseudo-Convolutional Policy Gradient for Sequence-to-Sequence
Lip-Reading [96.48553941812366]
Lip-reading aims to infer the speech content from the lip movement sequence.
Traditional learning process of seq2seq models suffers from two problems.
We propose a novel pseudo-convolutional policy gradient (PCPG) based method to address these two problems.
arXiv Detail & Related papers (2020-03-09T09:12:26Z) - Adaptive Correlated Monte Carlo for Contextual Categorical Sequence
Generation [77.7420231319632]
We adapt contextual generation of categorical sequences to a policy gradient estimator, which evaluates a set of correlated Monte Carlo (MC) rollouts for variance control.
We also demonstrate the use of correlated MC rollouts for binary-tree softmax models, which reduce the high generation cost in large vocabulary scenarios.
arXiv Detail & Related papers (2019-12-31T03:01:55Z)
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.