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.

由于乳腺癌的发生和死亡率很高，乳房X线照片中检测肿块很重要。在乳房X线照片质量检测中，对成对病变对应的建模特别重要。但是，大多数现有方法构建了相对粗糙的对应关系，并且尚未利用对应的监督。在本文中，我们提出了一个新的基于变压器的框架CL-NET，以端到端的方式学习病变检测和成对对应。在CL-NET中，提出了观察性病变检测器来实现跨视图候选者的动态相互作用，而病变接头则采用通信监督来更准确地指导相互作用过程。这两种设计的组合实现了对乳房X线照片的成对病变对应的精确理解。实验表明，CL-NET在公共DDSM数据集和我们的内部数据集上产生最先进的性能。此外，在低FPI制度中，它的表现优于先前的方法。

本文探讨了管状结构提取任务的点集表示。与传统的掩码表示相比，点集表示享有其灵活性和表示能力，这不会受到固定网格作为掩模的限制。受此启发，我们提出了PointCatter，这是管状结构提取任务的分割模型的替代方法。PointCatter将图像分为散射区域，并对每个散点区域预测点。我们进一步提出了基于贪婪的区域的两分匹配算法，以端到端训练网络。我们在四个公共管状数据集上基准测试了点刻表，并且有关管状结构分割和中心线提取任务的广泛实验证明了我们方法的有效性。代码可在https://github.com/zhangzhao2022/pointscatter上找到。

经过对人体跟踪系统引起的隐私问题的调查，我们提出了一种黑盒对抗攻击方法，该方法对最先进的人类检测模型，称为Invisibilitee。该方法学习了可打印的对抗图案，适用于T恤，这些T恤在人体跟踪系统前的物理世界中抓起佩戴者。我们设计了一种角度不足的学习方案，该方案利用了时尚数据集的分割和几何扭曲过程，因此生成的对抗模式可有效从所有摄像机角度和看不见的黑盒检测模型欺骗人检测器。数字环境和物理环境中的经验结果表明，随着Invisibilitee的启用，人体跟踪系统检测佩戴者的能力显着下降。

随着LIDAR传感器在自动驾驶中的流行率，3D对象跟踪受到了越来越多的关注。在点云序列中，3D对象跟踪旨在预测给定对象模板中连续帧中对象的位置和方向。在变压器成功的驱动下，我们提出了点跟踪变压器（PTTR），它有效地预测了高质量的3D跟踪，借助变压器操作，以粗到1的方式导致。 PTTR由三个新型设计组成。 1）我们设计的关系意识采样代替随机抽样，以在亚采样过程中保留与给定模板相关的点。 2）我们提出了一个点关系变压器，以进行有效的特征聚合和模板和搜索区域之间的特征匹配。 3）基于粗糙跟踪结果，我们采用了一个新颖的预测改进模块，通过局部特征池获得最终的完善预测。此外，以捕获对象运动的鸟眼视图（BEV）的有利特性（BEV）的良好属性，我们进一步设计了一个名为PTTR ++的更高级的框架，该框架既包含了点的视图和BEV表示）产生高质量跟踪结果的影响。 PTTR ++实质上提高了PTTR顶部的跟踪性能，并具有低计算开销。多个数据集的广泛实验表明，我们提出的方法达到了卓越的3D跟踪准确性和效率。

光保护综合技术的快速进展达到了真实和操纵图像之间的边界开始模糊的临界点。最近，一个由Mega-Scale Deep Face Forgery DataSet，由290万个图像组成和221,247个视频的伪造网络已被释放。它是迄今为止的数据规模，操纵（7个图像级别方法，8个视频级别方法），扰动（36个独立和更混合的扰动）和注释（630万个分类标签，290万操纵区域注释和221,247个时间伪造段标签）。本文报告了Forgerynet-Face Forgery Analysis挑战2021的方法和结果，它采用了伪造的基准。模型评估在私人测试集上执行离线。共有186名参加比赛的参与者，11名队伍提交了有效的提交。我们将分析排名排名的解决方案，并展示一些关于未来工作方向的讨论。

基于图像和视频的3D人类恢复（即姿势和形状估计）取得了实质性进展。但是，由于运动捕获的高度成本，现有的数据集通常受到规模和多样性的限制。在这项工作中，我们通过使用自动注释的3D地面真相玩电子游戏来获得大量的人类序列。具体来说，我们贡献了GTA-Human，这是一种由GTA-V游戏引擎生成的大规模3D人类数据集，具有高度多样化的主题，动作和场景。更重要的是，我们研究游戏玩法数据的使用并获得五个主要见解。首先，游戏数据非常有效。基于框架的简单基线对GTA-Human训练，其优于更复杂的方法的幅度很大。对于基于视频的方法，GTA-Human甚至与内域训练集相当。其次，我们发现合成数据为通常在室内收集的真实数据提供了关键补充。我们对域间隙的调查为简单但有用的数据混合策略提供了解释。第三，数据集的比例很重要。性能提升与可用的其他数据密切相关。一项系统的研究揭示了来自多个关键方面的数据密度的模型敏感性。第四，GTA-Human的有效性还归因于丰富的强制监督标签（SMPL参数），在实际数据集中获取否则它们很昂贵。第五，合成数据的好处扩展到较大的模型，例如更深层次的卷积神经网络（CNN）和变压器，也观察到了重大影响。我们希望我们的工作可以为将3D人类恢复到现实世界铺平道路。主页：https：//caizhongang.github.io/projects/gta-human/

Remote sensing of the Earth's surface water is critical in a wide range of environmental studies, from evaluating the societal impacts of seasonal droughts and floods to the large-scale implications of climate change. Consequently, a large literature exists on the classification of water from satellite imagery. Yet, previous methods have been limited by 1) the spatial resolution of public satellite imagery, 2) classification schemes that operate at the pixel level, and 3) the need for multiple spectral bands. We advance the state-of-the-art by 1) using commercial imagery with panchromatic and multispectral resolutions of 30 cm and 1.2 m, respectively, 2) developing multiple fully convolutional neural networks (FCN) that can learn the morphological features of water bodies in addition to their spectral properties, and 3) FCN that can classify water even from panchromatic imagery. This study focuses on rivers in the Arctic, using images from the Quickbird, WorldView, and GeoEye satellites. Because no training data are available at such high resolutions, we construct those manually. First, we use the RGB, and NIR bands of the 8-band multispectral sensors. Those trained models all achieve excellent precision and recall over 90% on validation data, aided by on-the-fly preprocessing of the training data specific to satellite imagery. In a novel approach, we then use results from the multispectral model to generate training data for FCN that only require panchromatic imagery, of which considerably more is available. Despite the smaller feature space, these models still achieve a precision and recall of over 85%. We provide our open-source codes and trained model parameters to the remote sensing community, which paves the way to a wide range of environmental hydrology applications at vastly superior accuracies and 2 orders of magnitude higher spatial resolution than previously possible.

We present NusaCrowd, a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have has brought together 137 datasets and 117 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their effectiveness has been demonstrated in multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and its local languages. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and its local languages. Our work is intended to help advance natural language processing research in under-represented languages.

Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image or video by introducing an additional high-resolution (HR) reference image. Existing Ref-SR methods mostly rely on implicit correspondence matching to borrow HR textures from reference images to compensate for the information loss in input images. However, performing local transfer is difficult because of two gaps between input and reference images: the transformation gap (e.g., scale and rotation) and the resolution gap (e.g., HR and LR). To tackle these challenges, we propose C2-Matching in this work, which performs explicit robust matching crossing transformation and resolution. 1) To bridge the transformation gap, we propose a contrastive correspondence network, which learns transformation-robust correspondences using augmented views of the input image. 2) To address the resolution gap, we adopt teacher-student correlation distillation, which distills knowledge from the easier HR-HR matching to guide the more ambiguous LR-HR matching. 3) Finally, we design a dynamic aggregation module to address the potential misalignment issue between input images and reference images. In addition, to faithfully evaluate the performance of Reference-based Image Super-Resolution under a realistic setting, we contribute the Webly-Referenced SR (WR-SR) dataset, mimicking the practical usage scenario. We also extend C2-Matching to Reference-based Video Super-Resolution task, where an image taken in a similar scene serves as the HR reference image. Extensive experiments demonstrate that our proposed C2-Matching significantly outperforms state of the arts on the standard CUFED5 benchmark and also boosts the performance of video SR by incorporating the C2-Matching component into Video SR pipelines.