在过去的十年中，基于学习的图像质量评估（IQA）取得了显着的进步，但几乎所有人都考虑了两个关键组成部分 - 模型和数据 - 相对隔离。具体而言，以模型为中心的IQA着重于在固定和广泛重复使用的数据集上开发“更好”的客观质量方法，并具有过度拟合的危险。以数据为中心的IQA涉及进行心理物理实验来构建“更好”的人类通知数据集，不幸的是，在数据集创建期间，它忽略了当前的IQA模型。在本文中，我们首先设计了一系列实验，以计算探测模型和数据的这种隔离会阻碍IQA的进一步进展。然后，我们描述一个集成了以模型为中心和数据的IQA的计算框架。作为一个具体示例，我们设计了计算模块，以量化基于盲人IQA（BIQA）模型预测和深度内容感知特征的候选图像的值得采样性。实验结果表明，所提出的值得采样的模块成功地发现了所检查的BIQA模型的各种故障，这些模型确实值得包括在下一代数据集中。

随着高动态范围（HDR）摄影的日益普及和可访问性，用于动态范围压缩和中等呈现的音调映射操作员（TMO）实际上是要求的。在本文中，我们开发了一种基于生物学的，计算效率和感知优化的两阶段神经网络图像TMO。在第一阶段，由人类视觉系统（HVS）早期阶段的生理学动机，我们首先将HDR图像分解为标准化的Laplacian金字塔。然后，我们使用两个轻巧的深神经网络（DNN），将这种归一化表示作为输入并估计相应LDR图像的拉普拉斯金字塔。我们通过最小化标准化的拉普拉斯金字塔距离（NLPD）来优化音调映射网络，这是一种对人类对音调映射图像质量判断的校准的感知度量。在第二阶段中，我们通过输入HDR图像``校准''，生成具有不同颜色饱和度和细节可见性的伪型曝光图像堆栈。然后，我们通过最大化MEF-SSIM的变体，这是另一个具有感知校准的度量以进行图像融合，将另一个轻巧的DNN训练将LDR图像堆叠融合到所需的LDR图像中。通过这样做，提出的TMO是完全自动的，以映射未校准的HDR图像。在一组独立的HDR图像中，我们发现我们的方法生成具有更好的视觉质量的图像，并且是本地最快的TMO之一。

虚拟现实（VR）视频（通常以360美元$^\ Circ $视频形式）由于VR技术的快速开发以及消费级360 $^\ Circ $摄像机和显示器的显着普及而引起了人们的关注。因此，了解人们如何看待用户生成的VR视频，这些视频可能会受到混乱的真实扭曲，通常是在时空和时间上局部的。在本文中，我们建立了最大的360美元$^\ Circ $视频数据库之一，其中包含502个用户生成的视频，内容丰富和失真多样性。我们捕获了139位用户的观看行为（即扫描路径），并在四个不同的观看条件下（两个起点$ \ times $ $ $ $ $两个探索时间）收集了他们的意见分数。我们对记录的数据提供了详尽的统计分析，从而产生了一些有趣的观察结果，例如观看条件对观看行为和感知质量的重大影响。此外，我们还探讨了我们的数据和分析的其他用法，包括评估360 $^\ CIRC $视频的质量评估和显着性检测的计算模型。我们已经在https://github.com/yao-yiru/vr-video-database上提供了数据集和代码。

虽然昼夜投影（ERP）是存储全向图像（也称为360度图像）的方便形式，但它既不是等区别也不是共形的，因此与随后的视觉通信不友好。在图像压缩的背景下，ERP将过度采样和变形和靠近杆子的东西，使得感知上最佳的比特分配难以实现。在传统的360度图像压缩中，引入了诸如区域明智的包装和平铺表示的技术以减轻过采样问题，实现有限的成功。在本文中，我们首次尝试学习用于全向图像压缩的深度神经网络之一。我们首先描述参数伪压花表示作为常见的伪变性地图突起的概括。提出了一种计算上易贪婪的方法，以确定关于速率失真性能的新型代理目标的假阴压表示的（子） - 优化配置。然后，我们提出了假阴压卷曲的360度图像压缩。在参数表示的合理约束下，可以通过标准卷积与所谓的假阴压填充有效地实现假阴压卷积。为了展示我们想法的可行性，我们实现了一个端到端的360度图像压缩系统，由学习的假阴短表示，分析变换，非均匀量化器，合成变换和熵模型组成。实验结果为19,790美元$ 9,790 $全向图像表明，我们的方法始终如一的比竞争方法达到更好的速率失真性能。此外，对于所有比特率的所有图像，我们的方法的视觉质量显着提高。

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. However, domain discrepancies in low-level image statistics and high-level contexts compromise the segmentation performance over the target domain. A key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. Unfortunately, there is a lack of such unified approaches for UDA tasks in the existing literature. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation. Concretely, for image-level domain shifts, we propose a global photometric alignment module and a global texture alignment module that align images in the source and target domains in terms of image-level properties. For feature-level domain shifts, we perform global manifold alignment by projecting pixel features from both domains onto the feature manifold of the source domain; and we further regularize category centers in the source domain through a category-oriented triplet loss and perform target domain consistency regularization over augmented target domain images. Experimental results demonstrate that our pipeline significantly outperforms previous methods. In the commonly tested GTA5$\rightarrow$Cityscapes task, our proposed method using Deeplab V3+ as the backbone surpasses previous SOTA by 8%, achieving 58.2% in mIoU.

Given the increasingly intricate forms of partial differential equations (PDEs) in physics and related fields, computationally solving PDEs without analytic solutions inevitably suffers from the trade-off between accuracy and efficiency. Recent advances in neural operators, a kind of mesh-independent neural-network-based PDE solvers, have suggested the dawn of overcoming this challenge. In this emerging direction, Koopman neural operator (KNO) is a representative demonstration and outperforms other state-of-the-art alternatives in terms of accuracy and efficiency. Here we present KoopmanLab, a self-contained and user-friendly PyTorch module of the Koopman neural operator family for solving partial differential equations. Beyond the original version of KNO, we develop multiple new variants of KNO based on different neural network architectures to improve the general applicability of our module. These variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman-Burgers equation) and ERA5 (i.e., one of the largest high-resolution data sets of global-scale climate fields). These demonstrations suggest the potential of KoopmanLab to be considered in diverse applications of partial differential equations.

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio. Specifically, we first develop a style encoder to extract dynamic facial motion patterns of a style reference video and then encode them into a style code. Afterward, we introduce a style-controllable decoder to synthesize stylized facial animations from the speech content and style code. In order to integrate the reference speaking style into generated videos, we design a style-aware adaptive transformer, which enables the encoded style code to adjust the weights of the feed-forward layers accordingly. Thanks to the style-aware adaptation mechanism, the reference speaking style can be better embedded into synthesized videos during decoding. Extensive experiments demonstrate that our method is capable of generating talking head videos with diverse speaking styles from only one portrait image and an audio clip while achieving authentic visual effects. Project Page: https://github.com/FuxiVirtualHuman/styletalk.

Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.

Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.