Visual Question Answering (VQA) models often perform poorly on out-of-distribution data and struggle on domain generalization. Due to the multi-modal nature of this task, multiple factors of variation are intertwined, making generalization difficult to analyze. This motivates us to introduce a virtual benchmark, Super-CLEVR, where different factors in VQA domain shifts can be isolated in order that their effects can be studied independently. Four factors are considered: visual complexity, question redundancy, concept distribution and concept compositionality. With controllably generated data, Super-CLEVR enables us to test VQA methods in situations where the test data differs from the training data along each of these axes. We study four existing methods, including two neural symbolic methods NSCL and NSVQA, and two non-symbolic methods FiLM and mDETR; and our proposed method, probabilistic NSVQA (P-NSVQA), which extends NSVQA with uncertainty reasoning. P-NSVQA outperforms other methods on three of the four domain shift factors. Our results suggest that disentangling reasoning and perception, combined with probabilistic uncertainty, form a strong VQA model that is more robust to domain shifts. The dataset and code are released at https://github.com/Lizw14/Super-CLEVR.

我们考虑了单个RGB图像的类别级别6D姿势估计的问题。我们的方法代表对象类别作为Cuboid网格，并了解每个网格顶点的神经特征激活的生成模型，以通过可区分的渲染执行姿势估计。基于渲染的方法的一个常见问题是，它们依靠边界框建议，这些建议不会传达有关对象3D旋转的信息，并且当对象被部分遮挡时不可靠。取而代之的是，我们引入了一种粗到1的优化策略，该策略利用渲染过程来估算一组稀疏的6D对象建议集，随后通过基于梯度的优化进行了完善。实现我们方法融合的关键是一种神经特征表示，训练了使用对比度学习的尺度和旋转不变的训练。我们的实验表明，与先前的工作相比，尤其是在强烈的部分遮挡下，类别级别的6D姿势估计性能增强。

增强了现实世界情景的稳健性已经被证明非常具有挑战性。一个原因是现有的鲁棒性基准是有限的，因为它们依赖于合成数据，或者它们只是将稳健性降低为数据集之间的概括，因此忽略各个滋扰因素的影响。在这项工作中，我们介绍了罗宾，是一个基准数据集，用于诊断视觉算法对现实世界中的个人滋扰的鲁棒性。罗宾在Pascal VOC 2012和Imagenet数据集中构建了10个刚性类别，并包括对象的分布示例3D姿势，形状，纹理，背景和天气状况。 Robin是丰富的注释，以实现图像分类，对象检测和3D姿势估计的基准模型。我们为许多流行的基线提供了结果，并进行了几个有趣的观察结果：1。与其他人相比，一些滋扰因素对性能有更强烈的负面影响。此外，对oodnuisance的负面影响取决于下游视觉任务。 2.利用强大数据增强的鲁棒性的目前的方法只有在现实世界的情况下只有边际效应，有时甚至会降低表现。 3.我们在鲁棒性方面，我们不会遵守卷积和变压器架构之间的任何显着差异。我们相信我们的数据集提供了丰富的试验台，以研究视觉算法的稳健性，并有助于大大推动该领域的前瞻性研究。

面部表现攻击检测（PAD）对于保护面部识别（FR）应用程序至关重要。 FR性能已被证明对某些人口统计学和非人口统计学组是不公平的。但是，面部垫的公平性是一个研究的问题，这主要是由于缺乏适当的注释数据。为了解决此问题，这项工作首先通过组合几个知名的PAD数据集，在其中提供了七个人类宣传的属性标签，从而提出了一个组合的注释数据集（CAAD-PAD）。然后，这项工作通过研究我们的CAAD-Pad上的四个面部垫方法，全面分析了一组面垫的公平及其与培训数据的性质和操作决策阈值分配（ODTA）的关系。同时代表垫子的公平性和绝对垫性能，我们引入了一种新颖的指标，即准确性平衡公平（ABF）。关于CAAD-PAD的广泛实验表明，训练数据和ODTA会引起性别，遮挡和其他属性组的不公平性。基于这些分析，我们提出了一种数据增强方法Fairswap，该方法旨在破坏身份/语义信息和指南模型以挖掘攻击线索而不是与属性相关的信息。详细的实验结果表明，Fairswap通常可以提高垫子性能和面部垫的公平性。

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.