Automatic prediction of fluorescently labeled organelles from label-free
transmitted light input images is an important, yet difficult task. The
traditional way to obtain fluorescence images is related to performing
biochemical labeling which is time-consuming and costly. Therefore, an
automatic algorithm to perform the task based on the label-free transmitted
light microscopy could be strongly beneficial. The importance of the task
motivated researchers from the France-BioImaging to organize the LightMyCells
challenge where the goal is to propose an algorithm that automatically predicts
the fluorescently labeled nucleus, mitochondria, tubulin, and actin, based on
the input consisting of bright field, phase contrast, or differential
interference contrast microscopic images. In this work, we present the
contribution of the AGHSSO team based on a carefully prepared and trained
encoder-decoder deep neural network that achieves a considerable score in the
challenge, being placed among the best-performing teams.

自动预测无标记透射光输入图像中的荧光标记细胞器是一项重要且困难的任务，本研究介绍了 AGHSSO 团队基于经过精心准备和训练的编码 - 解码深度神经网络在 LightMyCells 挑战中所取得的相当高得分，位居最佳表现团队之一。

基于块状编码器 - 解码器结构的自动透射光到荧光成像的转换：对 LightMyCells 挑战的贡献

Patch-Based Encoder-Decoder Architecture for Automatic Transmitted Light  to Fluorescence Imaging Transition: Contribution to the LightMyCells  Challenge

To automatically produce a brief yet expressive summary of a long video, an
automatic algorithm should start by resembling the human process of summary
generation. Prior work proposed supervised and unsupervised algorithms to train
models for learning the underlying behavior of humans by increasing modeling
complexity or craft-designing better heuristics to simulate human summary
generation process. In this work, we take a different approach by analyzing a
major cue that humans exploit for the summary generation; the nature and
intensity of actions.
We empirically observed that a frame is more likely to be included in
human-generated summaries if it contains a substantial amount of deliberate
motion performed by an agent, which is referred to as actionness. Therefore, we
hypothesize that learning to automatically generate summaries involves an
implicit knowledge of actionness estimation and ranking. We validate our
hypothesis by running a user study that explores the correlation between
human-generated summaries and actionness ranks. We also run a consensus and
behavioral analysis between human subjects to ensure reliable and consistent
results. The analysis exhibits a considerable degree of agreement among
subjects within obtained data and verifying our initial hypothesis.
Based on the study findings, we develop a method to incorporate actionness
data to explicitly regulate a learning algorithm that is trained for summary
generation. We assess the performance of our approach to four summarization
benchmark datasets and demonstrate an evident advantage compared to
state-of-the-art summarization methods.

通过分析动作的性质和强度，该论文提出了一种自动算法来生成视频摘要，并使用动作性估计来调节训练模型以提高摘要的生成效果，与现有摘要方法相比有明显优势。

基于动作评分的视频摘要

Video Summarization via Actionness Ranking

Human matting, high quality extraction of humans from natural images, is
crucial for a wide variety of applications. Since the matting problem is
severely under-constrained, most previous methods require user interactions to
take user designated trimaps or scribbles as constraints. This user-in-the-loop
nature makes them difficult to be applied to large scale data or time-sensitive
scenarios. In this paper, instead of using explicit user input constraints, we
employ implicit semantic constraints learned from data and propose an automatic
human matting algorithm (SHM). SHM is the first algorithm that learns to
jointly fit both semantic information and high quality details with deep
networks. In practice, simultaneously learning both coarse semantics and fine
details is challenging. We propose a novel fusion strategy which naturally
gives a probabilistic estimation of the alpha matte. We also construct a very
large dataset with high quality annotations consisting of 35,513 unique
foregrounds to facilitate the learning and evaluation of human matting.
Extensive experiments on this dataset and plenty of real images show that SHM
achieves comparable results with state-of-the-art interactive matting methods.

本文提出了一种自动的人像抠图算法（SHM），以数据学习的隐式语义约束为基础，利用深度卷积神经网络同时拟合语义信息和高质量细节，经实验证明，该算法效果与互动抠图方法相当，并构建了一个包含 35,513 个前景图的高质量注释数据集。