Satellite imaging generally presents a trade-off between the frequency of
acquisitions and the spatial resolution of the images. Super-resolution is
often advanced as a way to get the best of both worlds. In this work, we
investigate multi-image super-resolution of satellite image time series, i.e.
how multiple images of the same area acquired at different dates can help
reconstruct a higher resolution observation. In particular, we extend
state-of-the-art deep single and multi-image super-resolution algorithms, such
as SRDiff and HighRes-net, to deal with irregularly sampled Sentinel-2 time
series. We introduce BreizhSR, a new dataset for 4x super-resolution of
Sentinel-2 time series using very high-resolution SPOT-6 imagery of Brittany, a
French region. We show that using multiple images significantly improves
super-resolution performance, and that a well-designed temporal positional
encoding allows us to perform super-resolution for different times of the
series. In addition, we observe a trade-off between spectral fidelity and
perceptual quality of the reconstructed HR images, questioning future
directions for super-resolution of Earth Observation data.

通过使用多张卫星图像，本研究提出了一种针对 Sentinel-2 时间序列的多图像超分辨率重建算法，并展示了多图像对超分辨率性能的显著改善，同时探讨了光谱保真度和感知质量之间的权衡关系。

交叉传感器不规则采样 Sentinel-2 时间序列的超分辨率

Cross-sensor super-resolution of irregularly sampled Sentinel-2 time  series

We study visual question answering in a setting where the answer has to be
mined from a pool of relevant and irrelevant images given as a context. For
such a setting, a model must first retrieve relevant images from the pool and
answer the question from these retrieved images. We refer to this problem as
retrieval-based visual question answering (or RETVQA in short). The RETVQA is
distinctively different and more challenging than the traditionally-studied
Visual Question Answering (VQA), where a given question has to be answered with
a single relevant image in context. Towards solving the RETVQA task, we propose
a unified Multi Image BART (MI-BART) that takes a question and retrieved images
using our relevance encoder for free-form fluent answer generation. Further, we
introduce the largest dataset in this space, namely RETVQA, which has the
following salient features: multi-image and retrieval requirement for VQA,
metadata-independent questions over a pool of heterogeneous images, expecting a
mix of classification-oriented and open-ended generative answers. Our proposed
framework achieves an accuracy of 76.5% and a fluency of 79.3% on the proposed
dataset, namely RETVQA and also outperforms state-of-the-art methods by 4.9%
and 11.8% on the image segment of the publicly available WebQA dataset on the
accuracy and fluency metrics, respectively.

本文介绍了一种针对多张图片中远程问答的视觉问答模型，并提出了一个名为 RETVQA 的具有多图和检索需求的新数据集，使用 MI-BART 模型在该数据集上取得了良好的表现。

从图像池中挖掘答案：走向基于检索的视觉问答

Answer Mining from a Pool of Images: Towards Retrieval-Based Visual  Question Answering

Recently, deep learning based 3D face reconstruction methods have shown
promising results in both quality and efficiency.However, training deep neural
networks typically requires a large volume of data, whereas face images with
ground-truth 3D face shapes are scarce. In this paper, we propose a novel deep
3D face reconstruction approach that 1) leverages a robust, hybrid loss
function for weakly-supervised learning which takes into account both low-level
and perception-level information for supervision, and 2) performs multi-image
face reconstruction by exploiting complementary information from different
images for shape aggregation. Our method is fast, accurate, and robust to
occlusion and large pose. We provide comprehensive experiments on three
datasets, systematically comparing our method with fifteen recent methods and
demonstrating its state-of-the-art performance.

本文提出了一种使用深度学习技术进行 3D 面部重建的新方法，其中使用了混合损失函数进行弱监督学习，同时利用多个图像进行面部重建，实现了快速、准确、鲁棒的重建效果，对不同数据集进行了综合实验，并与 15 种现有方法进行了比较，证明了其性能处于领先地位。

弱监督学习下的精确三维人脸重构：从单张图像到图像集

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From  Single Image to Image Set

The task of multi-image cued story generation, such as visual storytelling
dataset (VIST) challenge, is to compose multiple coherent sentences from a
given sequence of images. The main difficulty is how to generate image-specific
sentences within the context of overall images. Here we propose a deep learning
network model, GLAC Net, that generates visual stories by combining
global-local (glocal) attention and context cascading mechanisms. The model
incorporates two levels of attention, i.e., overall encoding level and image
feature level, to construct image-dependent sentences. While standard attention
configuration needs a large number of parameters, the GLAC Net implements them
in a very simple way via hard connections from the outputs of encoders or image
features onto the sentence generators. The coherency of the generated story is
further improved by conveying (cascading) the information of the previous
sentence to the next sentence serially. We evaluate the performance of the GLAC
Net on the visual storytelling dataset (VIST) and achieve very competitive
results compared to the state-of-the-art techniques. Our code and pre-trained
models are available here.

本研究提出 GLAC Net 模型，结合全局 - 局部注意力和上下文级联机制，实现多图故事生成，模型在视觉叙事数据集上获得极具竞争力的结果。