用于视频的带条件解码器的神经表示增强

采用条件扩散解码器提升学习型图像编解码器的率-失真-感知灵活性

Enhancing the Rate-Distortion-Perception Flexibility of Learned Image Codecs with Conditional Diffusion Decoders

针对现有学习型图像压缩编解码器低码率下视觉效果不佳的问题，本文提出用条件扩散模型作解码器，可在解码端基于采样方法灵活调整失真与感知的权衡，提升率-失真-感知灵活性。

DE-CGAN：利用多样性增强条件生成对抗网络提升 rTMS 治疗预测

DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks

针对 rTMS 治疗中代表性不足的 fMRI 数据导致 DNN 预测不可靠的问题，提出 DE-CGAN 方法通过生成合成样本增强数据多样性，显著提升了分类模型性能。

Boosting CNN beyond Label in Inverse Problems

Convolutional neural networks (CNN) have been extensively used for inverse problems. However, their prediction error for unseen test data is difficult to estimate a priori since the neural networks are trained using only selected data and their architecture are largely considered a blackbox. This poses a fundamental challenge to neural networks for unsupervised learning or improvement beyond the label. In this paper, we show that the recent unsupervised learning methods such as Noise2Noise, Stein's unbiased risk estimator (SURE)-based denoiser, and Noise2Void are closely related to each other …

End-to-End Conditional GAN-based Architectures for Image Colourisation

In this work recent advances in conditional adversarial networks are investigated to develop an end-to-end architecture based on Convolutional Neural Networks (CNNs) to directly map realistic colours to an input greyscale image. Observing that existing colourisation methods sometimes exhibit a lack of colourfulness, this paper proposes a method to improve colourisation results. In particular, the method uses Generative Adversarial Neural Networks (GANs) and focuses on improvement of training stability to enable better generalisation in large multi-class image datasets. Additionally, the integr…

Blur-Attention: A boosting mechanism for non-uniform blurred image restoration

Dynamic scene deblurring is a challenging problem in computer vision. It is difficult to accurately estimate the spatially varying blur kernel by traditional methods. Data-driven-based methods usually employ kernel-free end-to-end mapping schemes, which are apt to overlook the kernel estimation. To address this issue, we propose a blur-attention module to dynamically capture the spatially varying features of non-uniform blurred images. The module consists of a DenseBlock unit and a spatial attention unit with multi-pooling feature fusion, which can effectively extract complex spatially varying…

Conditional Coding and Variable Bitrate for Practical Learned Video Coding

This paper introduces a practical learned video codec. Conditional coding and quantization gain vectors are used to provide flexibility to a single encoder/decoder pair, which is able to compress video sequences at a variable bitrate. The flexibility is leveraged at test time by choosing the rate and GOP structure to optimize a rate-distortion cost. Using the CLIC21 video test conditions, the proposed approach shows performance on par with HEVC.