In media industry, the demand of SDR-to-HDRTV up-conversion arises when users
possess HDR-WCG (high dynamic range-wide color gamut) TVs while most
off-the-shelf footage is still in SDR (standard dynamic range). The research
community has started tackling this low-level vision task by learning-based
approaches. When applied to real SDR, yet, current methods tend to produce dim
and desaturated result, making nearly no improvement on viewing experience.
Different from other network-oriented methods, we attribute such deficiency to
training set (HDR-SDR pair). Consequently, we propose new HDRTV dataset (dubbed
HDRTV4K) and new HDR-to-SDR degradation models. Then, it's used to train a
luminance-segmented network (LSN) consisting of a global mapping trunk, and two
Transformer branches on bright and dark luminance range. We also update
assessment criteria by tailored metrics and subjective experiment. Finally,
ablation studies are conducted to prove the effectiveness. Our work is
available at: this https URL

本研究通过构建一个新的 HDRTV 数据集和 HDR-to-SDR 模型来解决 SDR-to-HDRTV up-conversion 任务并使用一个基于亮度分段网络的新方法 (LSN) 来提高转换效果。并更新了相关评价指标用于量化和主观实验验证其效果。

使用新数据集和降采样模型进行实用的 SDR-to-HDRTV 上转换学习

Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and  Degradation Models

This paper aims to provide a comprehensive critical overview on how entities
and their interactions in Complex Networked Systems (CNS) are modelled across
disciplines as they approach their ultimate goal of creating a Digital Twin
(DT) that perfectly matches the reality. We propose a new framework to
conceptually compare diverse existing modelling paradigms from different
perspectives and create unified assessment criteria to assess their respective
capabilities of reaching such an ultimate goal. Using the proposed criteria, we
also appraise how far the reviewed current state-of-the-art approaches are from
the idealised DTs. We also identify and propose potential directions and ways
of building a DT-orientated CNS based on the convergence and integration of CNS
and DT utilising a variety of cross-disciplinary techniques.

本文旨在提供关于如何对复杂网络系统中的实体和交互进行建模的综述，以达到创建完美匹配现实的数字孪生的最终目标。我们提出了一个新的框架来比较不同学科中现有的建模范例，并创建统一的评估标准，以评估它们各自达到终极目标的能力。使用所提出的标准，我们还评估了当前最先进方法距离理想数字孪生的距离。我们还鉴定和提出了构建基于 CNS 和 DT 汇聚和融合的 DT 导向的 CNS 的潜在方向和方法，利用各种跨学科技术。

面向数字孪生的复杂网络系统及其动力学建模：综合调查

Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey

Previous storytelling approaches mostly focused on optimizing traditional
metrics such as BLEU, ROUGE and CIDEr. In this paper, we re-examine this
problem from a different angle, by looking deep into what defines a
realistically-natural and topically-coherent story. To this end, we propose
three assessment criteria: relevance, coherence and expressiveness, which we
observe through empirical analysis could constitute a "high-quality" story to
the human eye. Following this quality guideline, we propose a reinforcement
learning framework, ReCo-RL, with reward functions designed to capture the
essence of these quality criteria. Experiments on the Visual Storytelling
Dataset (VIST) with both automatic and human evaluations demonstrate that our
ReCo-RL model achieves better performance than state-of-the-art baselines on
both traditional metrics and the proposed new criteria.

论文重新审视了故事叙述的优化问题并提出了基于相关性，连贯性和表达性的三个评估标准。为此，提出了一个基于强化学习的框架，ReCo-RL，并在 Visual Storytelling 数据集上进行了实验验证其优越性。