Diffeomorphic image registration is a commonly used method to deform one
image to resemble another. While warping a single image to another is useful,
it can be advantageous to warp multiple images simultaneously, such as in
tracking the motion of the heart across a sequence of images. In this paper,
our objective is to propose a novel method capable of registering a group or
sequence of images to a target image, resulting in registered images that
appear identical and therefore have a low rank. Moreover, we aim for these
registered images to closely resemble the target image. Through experimental
evidence, we will demonstrate our method's superior efficacy in producing
low-rank groupwise deformations compared to other state-of-the-art approaches.

提出一种新颖的方法，可以将一组或序列图像注册到目标图像，产生低秩的注册图像，与目标图像非常相似，并通过实验证据展示该方法在产生低秩群体变形方面的优越性。

心脏 Cine MRI 动态跟踪的低秩群组变形

Low Rank Groupwise Deformations for Motion Tracking in Cardiac Cine MRI

Despite the recent advancement in the study of removing motion blur in an
image, it is still hard to deal with strong blurs. While there are limits in
removing blurs from a single image, it has more potential to use multiple
images, e.g., using an additional image as a reference to deblur a blurry
image. A typical setting is deburring an image using a nearby sharp image(s) in
a video sequence, as in the studies of video deblurring. This paper proposes a
better method to use the information present in a reference image. The method
does not need a strong assumption on the reference image. We can utilize an
alternative shot of the identical scene, just like in video deblurring, or we
can even employ a distinct image from another scene. Our method first matches
local patches of the target and reference images and then fuses their features
to estimate a sharp image. We employ a patch-based feature matching strategy to
solve the difficult problem of matching the blurry image with the sharp
reference. Our method can be integrated into pre-existing networks designed for
single image deblurring. The experimental results show the effectiveness of the
proposed method.

使用多个图像进行去模糊处理的新方法，通过匹配目标图像和参考图像的局部块并融合其特征来估计清晰图像，实验证明了该方法的有效性。

基于参考图的运动模糊去除：学习如何利用参考图像的锐度

Reference-based Motion Blur Removal: Learning to Utilize Sharpness in  the Reference Image

Generating engaging content has drawn much recent attention in the NLP
community. Asking questions is a natural way to respond to photos and promote
awareness. However, most answers to questions in traditional question-answering
(QA) datasets are factoids, which reduce individuals' willingness to answer.
Furthermore, traditional visual question generation (VQG) confines the source
data for question generation to single images, resulting in a limited ability
to comprehend time-series information of the underlying event. In this paper,
we propose generating engaging questions from multiple images. We present MVQG,
a new dataset, and establish a series of baselines, including both end-to-end
and dual-stage architectures. Results show that building stories behind the
image sequence enables models to generate engaging questions, which confirms
our assumption that people typically construct a picture of the event in their
minds before asking questions. These results open up an exciting challenge for
visual-and-language models to implicitly construct a story behind a series of
photos to allow for creativity and experience sharing and hence draw attention
to downstream applications.

本文提出基于多张图片生成引人入胜的问题的方法，所建立的 MVQG 数据集以及基于端到端和双阶段体系结构的一系列基线模型表明，建立图像序列背后的故事能够使模型生成更有趣的问题。

Multi-VQG：为多张图片生成引人入胜的问题

Multi-VQG: Generating Engaging Questions for Multiple Images

A recent source of concern for the security of neural networks is the
emergence of clean-label dataset poisoning attacks, wherein correctly labeled
poison samples are injected into the training dataset. While these poison
samples look legitimate to the human observer, they contain malicious
characteristics that trigger a targeted misclassification during inference. We
propose a scalable and transferable clean-label poisoning attack against
transfer learning, which creates poison images with their center close to the
target image in the feature space. Our attack, Bullseye Polytope, improves the
attack success rate of the current state-of-the-art by 26.75% in end-to-end
transfer learning, while increasing attack speed by a factor of 12. We further
extend Bullseye Polytope to a more practical attack model by including multiple
images of the same object (e.g., from different angles) when crafting the
poison samples. We demonstrate that this extension improves attack
transferability by over 16% to unseen images (of the same object) without using
extra poison samples.

研究发现，干净标签数据集污染攻击是神经网络安全的一个最近问题，该攻击会注入正确标记的毒样本，并会触发有针对性的错误分类；此文提出了 Bullseye Polytope，这是一种可扩展的并且可以迁移的混合污染攻击方式，在推广学习中创造出与目标图片接近的毒图片，攻击成功率比目前最先进的技术提高了 26.75％，而攻击速度提高了 12 倍以上；同时，该文还对 Bullseye Polytope 进行了延伸和改进，使用多个图片来手工编制污染样本，攻击迁移性得到 16％的提高，而不使用额外的毒样本。