基于DNN的视频对象检测（VOD）为自动驾驶和视频监视行业提供了重要的重要性和有希望的机会。但是，由于其实用性，可行性和强大的攻击效果，对抗贴片攻击在现场视觉任务中产生了巨大的关注。这项工作提出了Themis，这是一种软件/硬件系统，可防止对抗贴片，以实时稳健的视频对象检测。我们观察到，对抗斑块在具有非稳定预测的小区域中表现出极为局部的表面特征，因此提出了对抗区域检测算法，以消除对抗性效应。Themis还提出了一种系统的设计，以通过消除冗余计算和记忆运输来有效地支持该算法。实验结果表明，提出的方法可以有效地从可忽略的硬件开销中从对抗性攻击中恢复系统。

近年来，大规模的深层模型取得了巨大的成功，但巨大的计算复杂性和大规模的存储要求使其在资源限制设备中部署它们是一个巨大的挑战。作为模型压缩和加速度方法，知识蒸馏通过从教师探测器转移黑暗知识有效提高了小型模型的性能。然而，大多数基于蒸馏的检测方法主要模仿近边界盒附近的特征，这遭受了两个限制。首先，它们忽略边界盒外面的有益特征。其次，这些方法模仿一些特征，这些特征被教师探测器被错误地被视为背景。为了解决上述问题，我们提出了一种新颖的特征性 - 丰富的评分（FRS）方法，可以选择改善蒸馏过程中的广义可检测性的重要特征。所提出的方法有效地检索边界盒外面的重要特征，并消除边界盒内的有害特征。广泛的实验表明，我们的方法在基于锚和无锚探测器上实现了出色的性能。例如，具有Reset-50的RetinAnet在Coco2017数据集上达到39.7％，甚至超过基于Reset-101的教师检测器38.9％甚至超过0.8％。

在实践中，在实践中应用机器学习算法的瓶颈缺乏大规模标记的数据。转移学习是利用其他数据来改善下游性能的流行策略，但是找到最相关的数据可能是具有挑战性的。神经数据服务器（NDS）是一种为给定的下游任务提供相关数据的搜索引擎，以前已被提议解决此问题。 NDS使用经过数据源培训的专家组合，以估计每个源和下游任务之间的相似性。因此，每个用户的计算成本都随着来源的数量而增长。为了解决这些问题，我们提出了可扩展的神经数据服务器（SND），这是一种大规模搜索引擎，理论上可以索引数千个数据集以将相关的ML数据提供给最终用户。 SND在初始化过程中训练专家在中介数据集上的混合物，并通过与中介数据集的近距离表示数据源和下游任务。因此，随着新数据集添加到服务器中，SNDS用户产生的计算成本仍然固定。我们验证SND在许多现实世界任务上，发现SNDS推荐的数据改善了基线的下游任务性能。我们还通过显示其选择相关数据以在自然图像设置之外传输的能力来证明SND的可伸缩性。

虽然在巨大数据上培训的机器学习模型导致了几个领域的断路器，但由于限制数据的访问，他们在隐私敏感域中的部署仍然有限。在私有数据上具有隐私约束的生成模型可以避免此挑战，而是提供对私有数据的间接访问。我们提出DP-Sinkhorn，一种新的最优传输的生成方法，用于从具有差异隐私的私有数据学习数据分布。 DP-Sinkhorn以差别私人方式在模型和数据之间的模型和数据之间最小化陷阱的分歧，将计算上有效的近似值，并在模型和数据之间使用新技术来控制梯度估计的偏差差异的偏差折衷。与现有的培训方法不同，差异私人生成模型主要基于生成的对抗网络，我们不依赖于对抗性目标，这令人惊叹的难以优化，特别是在隐私约束所施加的噪声存在下。因此，DP-Sinkhorn易于训练和部署。通过实验，我们改进了多种图像建模基准的最先进，并显示了差异私有的信息RGB图像综合。项目页面：https：//nv-tlabs.github.io/dp-sinkhorn。

社交媒体意见两极分化的大量工作集中在媒体痕迹不同社区的立场（或正交信念）的平坦分类。我们在两个重要方面扩展了这项工作。首先，我们不仅检测到社区之间的分歧点，而且还检测到一致性点。换句话说，我们在存在重叠的情况下估计社区信念。其次，代替平坦的分类，我们考虑了层次的信念估计，在该估计中，社区可能会分层。例如，两个反对党可能在核心问题上不同意，但是在一方，尽管同意基本面，但在进一步的细节上可能会出现分歧。我们称由此产生的组合问题为分层重叠的信念估计问题。为了解决它，本文开发了一类新的无监督的非负矩阵分解（NMF）算法，我们称信仰结构化矩阵分解（BSMF）。我们提出的无监督算法捕获了潜在的信仰交叉点和差异性以及等级结构。我们讨论算法的属性，并在合成数据集和现实世界数据集上进行评估。在合成数据集中，我们的模型将误差降低了40％。在实际的Twitter痕迹中，它的准确性提高了约10％。该模型还可以在理智检查中实现96.08％的自洽性。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.