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.

In recent years, vision-centric perception has flourished in various autonomous driving tasks, including 3D detection, semantic map construction, motion forecasting, and depth estimation. Nevertheless, the latency of vision-centric approaches is too high for practical deployment (e.g., most camera-based 3D detectors have a runtime greater than 300ms). To bridge the gap between ideal research and real-world applications, it is necessary to quantify the trade-off between performance and efficiency. Traditionally, autonomous-driving perception benchmarks perform the offline evaluation, neglecting the inference time delay. To mitigate the problem, we propose the Autonomous-driving StreAming Perception (ASAP) benchmark, which is the first benchmark to evaluate the online performance of vision-centric perception in autonomous driving. On the basis of the 2Hz annotated nuScenes dataset, we first propose an annotation-extending pipeline to generate high-frame-rate labels for the 12Hz raw images. Referring to the practical deployment, the Streaming Perception Under constRained-computation (SPUR) evaluation protocol is further constructed, where the 12Hz inputs are utilized for streaming evaluation under the constraints of different computational resources. In the ASAP benchmark, comprehensive experiment results reveal that the model rank alters under different constraints, suggesting that the model latency and computation budget should be considered as design choices to optimize the practical deployment. To facilitate further research, we establish baselines for camera-based streaming 3D detection, which consistently enhance the streaming performance across various hardware. ASAP project page: https://github.com/JeffWang987/ASAP.

Previous studies have explored generating accurately lip-synced talking faces for arbitrary targets given audio conditions. However, most of them deform or generate the whole facial area, leading to non-realistic results. In this work, we delve into the formulation of altering only the mouth shapes of the target person. This requires masking a large percentage of the original image and seamlessly inpainting it with the aid of audio and reference frames. To this end, we propose the Audio-Visual Context-Aware Transformer (AV-CAT) framework, which produces accurate lip-sync with photo-realistic quality by predicting the masked mouth shapes. Our key insight is to exploit desired contextual information provided in audio and visual modalities thoroughly with delicately designed Transformers. Specifically, we propose a convolution-Transformer hybrid backbone and design an attention-based fusion strategy for filling the masked parts. It uniformly attends to the textural information on the unmasked regions and the reference frame. Then the semantic audio information is involved in enhancing the self-attention computation. Additionally, a refinement network with audio injection improves both image and lip-sync quality. Extensive experiments validate that our model can generate high-fidelity lip-synced results for arbitrary subjects.

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.

鉴于其广泛的应用，已经对人面部交换的任务进行了许多尝试。尽管现有的方法主要依赖于乏味的网络和损失设计，但它们仍然在源和目标面之间的信息平衡中挣扎，并倾向于产生可见的人工制品。在这项工作中，我们引入了一个名为StylesWap的简洁有效的框架。我们的核心想法是利用基于样式的生成器来增强高保真性和稳健的面部交换，因此可以采用发电机的优势来优化身份相似性。我们仅通过最小的修改来确定，StyleGAN2体系结构可以成功地处理来自源和目标的所需信息。此外，受到TORGB层的启发，进一步设计了交换驱动的面具分支以改善信息的融合。此外，可以采用stylegan倒置的优势。特别是，提出了交换引导的ID反转策略来优化身份相似性。广泛的实验验证了我们的框架会产生高质量的面部交换结果，从而超过了最先进的方法，既有定性和定量。

高质量的HDRI（高动态范围图像），通常是HDR Panoramas，是创建图形中3D场景的3D场景的最受欢迎的方法之一。考虑到捕获HDRI的困难，高度需要一种多功能和可控的生成模型，外行用户可以直观地控制生成过程。但是，现有的最新方法仍然难以合成复杂场景的高质量全景。在这项工作中，我们提出了一个零击文本驱动的框架Text2Light，以生成4K+分辨率HDRIS，而无需配对培训数据。给定一个自由形式的文本作为场景的描述，我们通过两个专用步骤合成相应的HDRI：1）在低动态范围（LDR）（LDR）和低分辨率的文本驱动全景生成，以及2）超分辨率逆音映射在分辨率和动态范围内扩大LDR Panorama。具体来说，为了获得零击文本驱动的全景生成，我们首先将双代码簿作为不同环境纹理的离散表示形式。然后，在预先训练的剪辑模型的驱动下，一个文本条件的全局采样器学会了根据输入文本从全局代码簿中采样整体语义。此外，一个结构感知的本地采样器学会了以整体语义为指导的LDR Panoramas逐个贴片。为了获得超分辨率的逆音映射，我们从LDR Panorama得出了360度成像的连续表示，作为一组固定在球体上的结构性潜在代码。这种连续表示可以使多功能模块同时提高分辨率和动态范围。广泛的实验证明了Text2light在产生高质量HDR全景方面具有卓越的能力。此外，我们还展示了我们在现实渲染和沉浸式VR中工作的可行性。

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。

如今，基础模型已成为人工智能中的基本基础设施之一，铺平了通往通用情报的方式。但是，现实提出了两个紧急挑战：现有的基础模型由英语社区主导；用户通常会获得有限的资源，因此不能总是使用基础模型。为了支持中文社区的发展，我们介绍了一个名为Fengshenbang的开源项目，该项目由认知计算与自然语言研究中心（CCNL）领导。我们的项目具有全面的功能，包括大型预培训模型，用户友好的API，基准，数据集等。我们将所有这些都包装在三个子项目中：风水次模型，风水框架和狂热基准。 Fengshenbang的开源路线图旨在重新评估中国预培训的大型大型模型的开源社区，促使整个中国大型模型社区的发展。我们还希望构建一个以用户为中心的开源生态系统，以允许个人访问所需的模型以匹配其计算资源。此外，我们邀请公司，大学和研究机构与我们合作建立大型开源模型的生态系统。我们希望这个项目将成为中国认知情报的基础。

神经表面重建旨在基于多视图图像重建准确的3D表面。基于神经量的先前方法主要训练完全隐式的模型，它们需要单个场景的数小时培训。最近的努力探讨了明确的体积表示，该表示通过记住可学习的素网格中的重要信息，从而大大加快了优化过程。但是，这些基于体素的方法通常在重建细粒几何形状方面遇到困难。通过实证研究，我们发现高质量的表面重建取决于两个关键因素：构建相干形状的能力和颜色几何依赖性的精确建模。特别是，后者是准确重建细节的关键。受这些发现的启发，我们开发了Voxurf，这是一种基于体素的方法，用于有效，准确的神经表面重建，该方法由两个阶段组成：1）利用可学习的特征网格来构建颜色场并获得连贯的粗糙形状，并且2）使用双色网络来完善详细的几何形状，可捕获精确的颜色几何依赖性。我们进一步引入了层次几何特征，以启用跨体素的信息共享。我们的实验表明，Voxurf同时达到了高效率和高质量。在DTU基准测试中，与最先进的方法相比，Voxurf获得了更高的重建质量，训练的加速度为20倍。