Advanced change detection techniques primarily target image pairs of equal
and high quality. However, variations in imaging conditions and platforms
frequently lead to image pairs with distinct qualities: one image being
high-quality, while the other being low-quality. These disparities in image
quality present significant challenges for understanding image pairs
semantically and extracting change features, ultimately resulting in a notable
decline in performance. To tackle this challenge, we introduce an innovative
training strategy grounded in knowledge distillation. The core idea revolves
around leveraging task knowledge acquired from high-quality image pairs to
guide the model's learning process when dealing with image pairs that exhibit
differences in quality. Additionally, we develop a hierarchical correlation
distillation approach (involving self-correlation, cross-correlation, and
global correlation). This approach compels the student model to replicate the
correlations inherent in the teacher model, rather than focusing solely on
individual features. This ensures effective knowledge transfer while
maintaining the student model's training flexibility.

应对图像品质差异带来的挑战，我们引入了一种基于知识蒸馏的创新训练策略，以高质量图像对获取的任务知识指导处理品质不同的图像对的学习过程。此外，我们开发了一种层次相关性蒸馏方法，通过复制教师模型中固有的相关性，而不仅仅关注个体特征，来确保有效地传递知识并保持学生模型的训练灵活性。

HiCD: 基于分层相关蒸馏的质量变化图像变化检测

HiCD: Change Detection in Quality-Varied Images via Hierarchical  Correlation Distillation

There is a growing interest in developing computer vision methods that can
learn from limited supervision. In this paper, we consider the problem of
learning to predict camera viewpoints, where obtaining ground-truth annotations
are expensive and require special equipment, from a limited number of labeled
images. We propose a semi-supervised viewpoint estimation method that can learn
to infer viewpoint information from unlabeled image pairs, where two images
differ by a viewpoint change. In particular our method learns to synthesize the
second image by combining the appearance from the first one and viewpoint from
the second one. We demonstrate that our method significantly improves the
supervised techniques, especially in the low-label regime and outperforms the
state-of-the-art semi-supervised methods.

本文研究了基于图像对的半监督视角估计方法，该方法可以从未标注的图像对中推断视角信息，并将其应用于视角预测任务。实验表明，该方法在低标注数据情况下表现出色，在半监督视角估计方法中也取得了最佳效果。

具有几何感知的条件生成半监督视角估计

Semi-supervised Viewpoint Estimation with Geometry-aware Conditional  Generation

We present a technique for synthesizing a motion blurred image from a pair of
unblurred images captured in succession. To build this system we motivate and
design a differentiable "line prediction" layer to be used as part of a neural
network architecture, with which we can learn a system to regress from image
pairs to motion blurred images that span the capture time of the input image
pair. Training this model requires an abundance of data, and so we design and
execute a strategy for using frame interpolation techniques to generate a
large-scale synthetic dataset of motion blurred images and their respective
inputs. We additionally capture a high quality test set of real motion blurred
images, synthesized from slow motion videos, with which we evaluate our model
against several baseline techniques that can be used to synthesize motion blur.
Our model produces higher accuracy output than our baselines, and is
significantly faster than baselines with competitive accuracy.

本文通过神经网络和线性预测技术，提出了一种技术，可以从一对连续拍摄的图像中合成运动模糊图像。作者还利用了帧内插技术生成了大规模的合成数据集，并从慢动作视频中捕获了高质量的测试集。实验结果显示，本文提出的模型在精度和速度上均优于基线技术。