Video action segmentation aims to slice the video into several action segments. Recently, timestamp supervision has received much attention due to lower annotation costs. We find the frames near the boundaries of action segments are in the transition region between two consecutive actions and have unclear semantics, which we call ambiguous intervals. Most existing methods iteratively generate pseudo-labels for all frames in each video to train the segmentation model. However, ambiguous intervals are more likely to be assigned with noisy and incorrect pseudo-labels, which leads to performance degradation. We propose a novel framework to train the model under timestamp supervision including the following two parts. First, pseudo-label ensembling generates pseudo-label sequences with ambiguous intervals, where the frames have no pseudo-labels. Second, iterative clustering iteratively propagates the pseudo-labels to the ambiguous intervals by clustering, and thus updates the pseudo-label sequences to train the model. We further introduce a clustering loss, which encourages the features of frames within the same action segment more compact. Extensive experiments show the effectiveness of our method.

很少有学习模型学习人类注释有限，而这种学习范式在各种任务中证明了实用性数据使该模型无法充分探索语义信息。为了解决这个问题，我们将知识蒸馏引入了几个弹出的对象检测学习范式。我们进一步进行了激励实验，该实验表明，在知识蒸馏的过程中，教师模型的经验误差将少数拍物对象检测模型的预测性能（作为学生）退化。为了了解这种现象背后的原因，我们从因果理论的角度重新审视了几个对象检测任务上知识蒸馏的学习范式，并因此发展了一个结构性因果模型。遵循理论指导，我们建议使用基于后门调整的知识蒸馏方法，用于少数拍物检测任务，即Disentangle和Remerge（D＆R），以对相应的结构性因果模型进行有条件的因果干预。从理论上讲，我们为后门标准提供了扩展的定义，即一般后门路径，可以在特定情况下扩展后门标准的理论应用边界。从经验上讲，多个基准数据集上的实验表明，D＆R可以在几个射击对象检测中产生显着的性能提升。

流行的图神经网络模型在图表学习方面取得了重大进展。但是，在本文中，我们发现了一个不断被忽视的现象：用完整图测试的预训练的图表学习模型的表现不佳，该模型用良好的图表测试。该观察结果表明，图中存在混杂因素，这可能会干扰模型学习语义信息，而当前的图表表示方法并未消除其影响。为了解决这个问题，我们建议强大的因果图表示学习（RCGRL）学习可靠的图形表示，以防止混杂效应。 RCGRL引入了一种主动方法，可以在无条件的力矩限制下生成仪器变量，该方法使图表学习模型能够消除混杂因素，从而捕获与下游预测有因果关系的歧视性信息。我们提供定理和证明，以保证拟议方法的理论有效性。从经验上讲，我们对合成数据集和多个基准数据集进行了广泛的实验。结果表明，与最先进的方法相比，RCGRL实现了更好的预测性能和泛化能力。

最近的作品以自我监督的方式探索学习图表表示。在图形对比学习中，基准方法应用各种图形增强方法。但是，大多数增强方法都是不可学习的，这导致发出不束缚的增强图。这种增强可以缩短曲线图对比学学习方法的表现能力。因此，我们激励我们的方法通过可学习的图形增强器来生成增强图，称为元图形增强器（Mega）。然后，我们阐明了“良好”的图形增强必须在特征级别的实例级别和信息性上具有均匀性。为此，我们提出了一种新颖的方法来学习图形增强者，可以以统一和信息性产生增强。图表增强器的目的是促进我们的特征提取网络，以学习更辨别的特征表示，这激励我们提出元学范式。经验上，多个基准数据集的实验表明，Mega优于图形自我监督学习任务中的最先进的方法。进一步的实验研究证明了巨型术语的有效性。

虽然视觉变形金机在许多视觉任务中实现了骨干模型的优异性能，但大多数都打算捕获图像或窗口中所有令牌的全局关系，这会破坏2D结构中的补丁之间固有的空间和本地相关性。在本文中，我们介绍了一个名为SimVit的简单视觉变压器，将空间结构和本地信息合并到视觉变压器中。具体而言，我们引入多头中央自我关注（MCSA）而不是传统的多头自我关注以捕获高度局部关系。滑动窗口的引入有助于捕获空间结构。同时，SIMVIT从不同层提取多尺度分层特征以进行密集的预测任务。广泛的实验表明，SIMVIT作为各种图像处理任务的通用骨干模型是有效和高效的。特别是，我们的SIMVIT-MICRO只需要3.3M的参数，在Imagenet-1K数据集上达到71.1％的前1个精度，即现在是最小的尺寸视觉变压器模型。我们的代码将在https://github.com/cucasligang/simvit中提供。

Deep networks for computer vision are not reliable when they encounter adversarial examples. In this paper, we introduce a framework that uses the dense intrinsic constraints in natural images to robustify inference. By introducing constraints at inference time, we can shift the burden of robustness from training to the inference algorithm, thereby allowing the model to adjust dynamically to each individual image's unique and potentially novel characteristics at inference time. Among different constraints, we find that equivariance-based constraints are most effective, because they allow dense constraints in the feature space without overly constraining the representation at a fine-grained level. Our theoretical results validate the importance of having such dense constraints at inference time. Our empirical experiments show that restoring feature equivariance at inference time defends against worst-case adversarial perturbations. The method obtains improved adversarial robustness on four datasets (ImageNet, Cityscapes, PASCAL VOC, and MS-COCO) on image recognition, semantic segmentation, and instance segmentation tasks. Project page is available at equi4robust.cs.columbia.edu.

Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in graph, their ST patterns can vary greatly in difficulties for modeling, owning to the heterogeneous nature of ST data. We argue that unveiling the nodes to the model in a meaningful order, from easy to complex, can provide performance improvements over traditional training procedure. The idea has its root in Curriculum Learning which suggests in the early stage of training models can be sensitive to noise and difficult samples. In this paper, we propose ST-Curriculum Dropout, a novel and easy-to-implement strategy for spatial-temporal graph modeling. Specifically, we evaluate the learning difficulty of each node in high-level feature space and drop those difficult ones out to ensure the model only needs to handle fundamental ST relations at the beginning, before gradually moving to hard ones. Our strategy can be applied to any canonical deep learning architecture without extra trainable parameters, and extensive experiments on a wide range of datasets are conducted to illustrate that, by controlling the difficulty level of ST relations as the training progresses, the model is able to capture better representation of the data and thus yields better generalization.

Deep learning-based full-reference image quality assessment (FR-IQA) models typically rely on the feature distance between the reference and distorted images. However, the underlying assumption of these models that the distance in the deep feature domain could quantify the quality degradation does not scientifically align with the invariant texture perception, especially when the images are generated artificially by neural networks. In this paper, we bring a radical shift in inferring the quality with learned features and propose the Deep Image Dependency (DID) based FR-IQA model. The feature dependency facilitates the comparisons of deep learning features in a high-order manner with Brownian distance covariance, which is characterized by the joint distribution of the features from reference and test images, as well as their marginal distributions. This enables the quantification of the feature dependency against nonlinear transformation, which is far beyond the computation of the numerical errors in the feature space. Experiments on image quality prediction, texture image similarity, and geometric invariance validate the superior performance of our proposed measure.

凝视估计对于许多科学领域和日常应用至关重要，范围从认知心理学的基本研究到注意力吸引人的移动系统。尽管深度学习的最新进展在建立高度准确的凝视估计系统方面取得了巨大的成功，但相关的高计算成本以及对大规模标记的凝视数据的依赖，以实现对现有解决方案实际使用的监督学习地点挑战。为了超越这些局限性，我们提出了FreeGaze，这是一种用于无监督的注视表示学习的资源有效框架。 FreeGaze在其设计中结合了频域目光的估计和对比度注视表示。前者大大减轻了系统校准和凝视估计中的计算负担，并大大减少了系统延迟。尽管后者克服了现有基于学习的同行的数据标记障碍，并确保在没有凝视标签的情况下确保有效的凝视表示学习。我们对两个凝视估计数据集的评估表明，通过现有基于监督的学习方法，FreeGaze可以在系统校准和注视估计中分别实现高达6.81和1.67倍的速度，以实现可比较的凝视估计精度。

自然图像的统计规律（称为自然场景统计数据）在不引用图像质量评估中起重要作用。但是，人们普遍认为，通常是计算机生成的屏幕内容图像（SCI）不持有此类统计信息。在这里，我们首次尝试学习SCI的统计数据，基于可以有效确定SCI的质量。所提出的方法的基本机制是基于一个狂野的假设，即没有物理上获得的SCI仍然遵守某些可以以学习方式理解的统计数据。我们从经验上表明，在质量评估中可以有效利用统计偏差，并且在不同的环境中进行评估时，提出的方法优越。广泛的实验结果表明，与现有的NR-IQA模型相比，基于深度统计的SCI质量评估（DFSS-IQA）模型可提供有希望的性能，并在跨数据库设置中显示出很高的概括能力。我们的方法的实现可在https://github.com/baoliang93/dfss-iqa上公开获得。