Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

变压器注意机制中的设计选择，包括弱电感偏置和二次计算复杂性，限制了其用于建模长序列的应用。在本文中，我们介绍了一个简单的，理论上的，单头的门控注意机制，配备了（指数）移动平均线，以将局部依赖性的电感偏置纳入位置 - 敏锐的注意机制中。我们进一步提出了一个具有线性时间和空间复杂性的大型变体，但通过将整个序列分为固定长度的多个块，仅产生最小的质量损失。对广泛的序列建模基准测试的广泛实验，包括远距离竞技场，神经机器翻译，自动回归语言建模以及图像和语音分类，表明，巨人比其他序列模型取得了重大改进，包括变种物的变体和最新的变体模型状态空间模型。

无监督的视频域适应是一项实用但具有挑战性的任务。在这项工作中，我们第一次从脱离视图中解决了它。我们的关键想法是在适应过程中将与域相关的信息从数据中删除。具体而言，我们考虑从两组潜在因素中生成跨域视频，一个编码静态域相关信息，另一个编码时间和语义相关的信息。然后开发转移顺序的VAE（Transvae）框架以建模这种产生。为了更好地适应适应，我们进一步提出了几个目标，以限制Transvae中的潜在因素。与几种最先进的方法相比，对UCF-HMDB，小丑和Epic-Kitchens数据集进行了广泛的实验验证了Transvae的有效性和优势。代码可在https://github.com/ldkong1205/transvae上公开获取。

由于具有高生物学合理性和低能消耗在神经形态硬件上的特性，因此尖峰神经网络（SNN）非常重要。作为获得深SNN的有效方法，转化方法在各种大型数据集上表现出高性能。但是，它通常遭受严重的性能降解和高时间延迟。特别是，以前的大多数工作都集中在简单的分类任务上，同时忽略了与ANN输出的精确近似。在本文中，我们首先从理论上分析转换误差，并得出时间变化极端对突触电流的有害影响。我们提出尖峰校准（Spicalib），以消除离散尖峰对输出分布的损坏，并修改脂肪，以使任意最大化层无损地转换。此外，提出了针对最佳标准化参数的贝叶斯优化，以避免经验设置。实验结果证明了分类，对象检测和分割任务的最新性能。据我们所知，这是第一次获得与ANN同时在这些任务上相当的SNN。此外，我们只需要先前在检测任务上工作的1/50推理时间，并且可以在0.492 $ \ times $ $下在分段任务上实现相同的性能。

我们从一组未配对的清晰和朦胧的图像中提供了实用的基于学习的图像飞行网络。本文提供了一种新的观点，可以将图像除去作为两类分离的因子分离任务，即清晰图像重建的任务相关因素以及与雾霾相关的分布的任务含量。为了在深度特征空间中实现这两类因素的分离，将对比度学习引入了一个自行车框架中，以通过指导与潜在因素相关的生成的图像来学习分离的表示形式。通过这种表述，提出的对比度拆除的脱掩护方法（CDD-GAN）采用负面发电机与编码器网络合作以交替进行更新，以产生挑战性负面对手的队列。然后，这些负面的对手是端到端训练的，以及骨干代表网络，以通过最大化对抗性对比损失来增强歧视性信息并促进因素分离性能。在培训期间，我们进一步表明，硬性负面例子可以抑制任务 - 无关紧要的因素和未配对的清晰景象可以增强与任务相关的因素，以便更好地促进雾霾去除并帮助图像恢复。对合成和现实世界数据集的广泛实验表明，我们的方法对现有的未配对飞行基线的表现良好。

创建视觉布局是图形设计的重要步骤。当我们寻求比例和多样化的视觉设计时，这种布局的自动生成很重要。在自动布局的作品上，专注于无条件生成，其中模型在忽略用户需要进行特定问题的同时生成布局。为了提前有条件布局，我们介绍了BLT，双向布局变压器。 BLT与自回归解码不同，因为它首先生成满足用户输入的布局，然后迭代地改进布局。我们验证了具有各种保真度量的多个基准测试模型。我们的结果表明，最先进的布局变压器模型的两个主要进步。首先，我们的模型授权布局变压器来满足可控布局的制作。其次，我们的模型削减了自回归解码的线性推理时间达到恒定的复杂度，从而在推理时间以制定布局实现4x-10x的加速。

变压器注意机制的二次计算和内存复杂性限制了对长序列建模的可扩展性。在本文中，我们提出了Luna，一种线性统一嵌套关注机制，使Softmax注意力具有两个嵌套线性关注功能，仅产生线性（与二次）的时间和空间复杂度相反。具体地，通过第一注意功能，LUNA将输入序列包装成固定长度的序列。然后，使用第二关注功能未包装包装序列。与更传统的关注机制相比，LUNA引入具有固定长度的附加序列作为输入和额外的相应输出，允许LUNA线性地进行关注操作，同时还存储足够的上下文信息。我们对三个序列建模任务的基准进行了广泛的评估：长上下文序列建模，神经机平移和大型预磨损的屏蔽语言建模。竞争甚至更好的实验结果表明了Luna的有效性和效率与各种各样相比

In contrast to the control-theoretic methods, the lack of stability guarantee remains a significant problem for model-free reinforcement learning (RL) methods. Jointly learning a policy and a Lyapunov function has recently become a promising approach to ensuring the whole system with a stability guarantee. However, the classical Lyapunov constraints researchers introduced cannot stabilize the system during the sampling-based optimization. Therefore, we propose the Adaptive Stability Certification (ASC), making the system reach sampling-based stability. Because the ASC condition can search for the optimal policy heuristically, we design the Adaptive Lyapunov-based Actor-Critic (ALAC) algorithm based on the ASC condition. Meanwhile, our algorithm avoids the optimization problem that a variety of constraints are coupled into the objective in current approaches. When evaluated on ten robotic tasks, our method achieves lower accumulated cost and fewer stability constraint violations than previous studies.

A storyboard is a roadmap for video creation which consists of shot-by-shot images to visualize key plots in a text synopsis. Creating video storyboards however remains challenging which not only requires association between high-level texts and images, but also demands for long-term reasoning to make transitions smooth across shots. In this paper, we propose a new task called Text synopsis to Video Storyboard (TeViS) which aims to retrieve an ordered sequence of images to visualize the text synopsis. We construct a MovieNet-TeViS benchmark based on the public MovieNet dataset. It contains 10K text synopses each paired with keyframes that are manually selected from corresponding movies by considering both relevance and cinematic coherence. We also present an encoder-decoder baseline for the task. The model uses a pretrained vision-and-language model to improve high-level text-image matching. To improve coherence in long-term shots, we further propose to pre-train the decoder on large-scale movie frames without text. Experimental results demonstrate that our proposed model significantly outperforms other models to create text-relevant and coherent storyboards. Nevertheless, there is still a large gap compared to human performance suggesting room for promising future work.