computer-vision-10

Historical Color Image Dating

3260 papers • 126 benchmarks • 313 datasets

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Historical Color Image Dataset

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Most implemented papers

Deep Ordinal Regression with Label Diversity

Axel Berg, M. Oskarsson, M. O'Connor•Sun Jun 28 2020

This work proposes that using several discrete data representations simultaneously can improve neural network learning compared to a single representation and test the approach on three challenging tasks to show that it reduces the prediction error.

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Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression

Jiwen Lu, Jie Zhou, Jianjiang Feng, Wanhua Li, Xiaoke Huang•Wed Mar 24 2021

Uncertainty is the only certainty there is. Modeling data uncertainty is essential for regression, especially in unconstrained settings. Traditionally the direct regression formulation is considered and the uncertainty is modeled by modifying the output space to a certain family of probabilistic distributions. On the other hand, classification based regression and ranking based solutions are more popular in practice while the direct regression methods suffer from the limited performance. How to model the uncertainty within the present-day technologies for regression remains an open issue. In this paper, we propose to learn probabilistic ordinal embeddings which represent each data as a multivariate Gaussian distribution rather than a deterministic point in the latent space. An ordinal distribution constraint is proposed to exploit the ordinal nature of regression. Our probabilistic ordinal embeddings can be integrated into popular regression approaches and empower them with the ability of uncertainty estimation. Experimental results show that our approach achieves competitive performance. Code is available at https://github.com/Li-Wanhua/POEs.

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OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression

Jiwen Lu, Yansong Tang, Jie Zhou, Xiu Li, Wanhua Li, Xiaoke Huang, Zheng Hua Zhu•Sun Jun 05 2022

This paper reformulates this task as an image-language matching problem with a contrastive objective, which regards labels as text and obtains a language prototype from a text encoder for each rank, and proposes OrdinalCLIP, a differentiable prompting method for adapting CLIP for ordinal regression.

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