使用诸如嵌入惯性测量单元（IMU）传感器的可穿戴设备（如智能手表）的人类活动识别（Har）具有与我们日常生活相关的各种应用，例如锻炼跟踪和健康监控。在本文中，我们使用在不同身体位置佩戴的多个IMU传感器提出了一种基于人类活动识别的新颖性方法。首先，设计传感器设计特征提取模块以提取具有卷积神经网络（CNNS）的各个传感器的最辨别特征。其次，开发了一种基于注意的融合机制，以了解不同车身位置处的传感器的重要性，并产生细节特征表示。最后，应用传感器间特征提取模块来学习与分类器连接的传感器间相关性以输出预测的活动。所提出的方法是使用五个公共数据集进行评估，并且在各种活动类别上优于最先进的方法。

在由车辆安装的仪表板摄像机捕获的视频中检测危险交通代理（仪表板）对于促进在复杂环境中的安全导航至关重要。与事故相关的视频只是驾驶视频大数据的一小部分，并且瞬态前的事故流程具有高度动态和复杂性。此外，风险和非危险交通代理的外观可能相似。这些使驾驶视频中的风险对象本地化特别具有挑战性。为此，本文提出了一个注意力引导的多式功能融合网络（AM-NET），以将仪表板视频的危险交通代理本地化。两个封闭式复发单元（GRU）网络使用对象边界框和从连续视频帧中提取的光流功能来捕获时空提示，以区分危险交通代理。加上GRUS的注意力模块学会了与事故相关的交通代理。融合了两个功能流，AM-NET预测了视频中交通代理的风险评分。在支持这项研究的过程中，本文还引入了一个名为“风险对象本地化”（ROL）的基准数据集。该数据集包含带有事故，对象和场景级属性的空间，时间和分类注释。拟议的AM-NET在ROL数据集上实现了85.73％的AUC的有希望的性能。同时，AM-NET在DOTA数据集上优于视频异常检测的当前最新视频异常检测。一项彻底的消融研究进一步揭示了AM-NET通过评估其不同组成部分的贡献的优点。

空中机器人（例如无人机）已被利用进行桥梁检查。可以通过板载摄像机收集具有可识别的结构元素和明显表面缺陷的检查图像，以提供有价值的信息以进行条件评估。本文旨在确定用于在检查图像中解析多类桥梁元素的合适的深神经网络（DNN）。一组广泛的定量评估以及定性示例表明，高分辨率净（HRNET）具有所需的能力。通过数据增强和130张图像的训练样本，预先训练的HRNET有效地转移到结构元件解析的任务中，并达到了92.67％的平均F1得分和86.33％的平均值。

传感器技术和人工智能的快速进步正在为交通安全增强创造新的机遇。仪表板相机（Dashcams）已广泛部署在人类驾驶车辆和自动驾驶车辆上。可以准确和迅速地预测来自Dashcam视频的事故的计算智能模型将增强事故预防的准备。交通代理的空间时间相互作用复杂。预测未来事故的视觉提示深深嵌入了Dashcam视频数据中。因此，交通事故的早期期待仍然是一个挑战。受到人类在视觉感知事故风险中的注意力行为的启发，提出了一种动态的空间 - 时间关注（DSTA）网络，用于从Dashcam视频的早期事故预期。 DSTA网络学习用动态时间关注（DTA）模块来选择视频序列的判别时间片段。它还学会专注于带有动态空间注意（DSA）模块的帧的信息空间区域。门控复发单元（GRU）与注意模块共同培训，以预测未来事故的可能性。在两个基准数据集上对DSTA网络的评估确认它已超过最先进的性能。一种彻底的消融研究，评估组件级别的DSTA网络揭示了网络如何实现这种性能。此外，本文提出了一种从两个互补模型中融合预测分数的方法，并验证其有效性进一步提高早期事故预期的性能。

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding. In order to combine inductive inference capability from the original KGE model and analogical inference capability enhanced by AnKGE, we interpolate the analogy score with the base model score and introduce the adaptive weights in the score function for prediction. Through extensive experiments on FB15k-237 and WN18RR datasets, we show that AnKGE achieves competitive results on link prediction task and well performs analogical inference.

When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task -- the task ``features" -- as well as how to combine these features into a single objective. If they try to do both at once from input designed to teach the full reward function, it is easy to end up with a representation that contains spurious correlations in the data, which fails to generalize to new settings. Instead, our ultimate goal is to enable robots to identify and isolate the causal features that people actually care about and use when they represent states and behavior. Our idea is that we can tune into this representation by asking users what behaviors they consider similar: behaviors will be similar if the features that matter are similar, even if low-level behavior is different; conversely, behaviors will be different if even one of the features that matter differs. This, in turn, is what enables the robot to disambiguate between what needs to go into the representation versus what is spurious, as well as what aspects of behavior can be compressed together versus not. The notion of learning representations based on similarity has a nice parallel in contrastive learning, a self-supervised representation learning technique that maps visually similar data points to similar embeddings, where similarity is defined by a designer through data augmentation heuristics. By contrast, in order to learn the representations that people use, so we can learn their preferences and objectives, we use their definition of similarity. In simulation as well as in a user study, we show that learning through such similarity queries leads to representations that, while far from perfect, are indeed more generalizable than self-supervised and task-input alternatives.

Vision-language models (VLMs) that are pre-trained on large-scale image-text pairs have demonstrated impressive transferability on a wide range of visual tasks. Transferring knowledge from such powerful pre-trained VLMs is emerging as a promising direction for building effective video recognition models. However, the current exploration is still limited. In our opinion, the greatest charm of pre-trained vision-language models is to build a bridge between visual and textual domains. In this paper, we present a novel framework called BIKE which utilizes the cross-modal bridge to explore bidirectional knowledge: i) We propose a Video Attribute Association mechanism which leverages the Video-to-Text knowledge to generate textual auxiliary attributes to complement video recognition. ii) We also present a Temporal Concept Spotting mechanism which uses the Text-to-Video expertise to capture temporal saliency in a parameter-free manner to yield enhanced video representation. The extensive studies on popular video datasets (ie, Kinetics-400 & 600, UCF-101, HMDB-51 and ActivityNet) show that our method achieves state-of-the-art performance in most recognition scenarios, eg, general, zero-shot, and few-shot video recognition. To the best of our knowledge, our best model achieves a state-of-the-art accuracy of 88.4% on challenging Kinetics-400 with the released CLIP pre-trained model.

There is a growing interest in developing unlearnable examples (UEs) against visual privacy leaks on the Internet. UEs are training samples added with invisible but unlearnable noise, which have been found can prevent unauthorized training of machine learning models. UEs typically are generated via a bilevel optimization framework with a surrogate model to remove (minimize) errors from the original samples, and then applied to protect the data against unknown target models. However, existing UE generation methods all rely on an ideal assumption called label-consistency, where the hackers and protectors are assumed to hold the same label for a given sample. In this work, we propose and promote a more practical label-agnostic setting, where the hackers may exploit the protected data quite differently from the protectors. E.g., a m-class unlearnable dataset held by the protector may be exploited by the hacker as a n-class dataset. Existing UE generation methods are rendered ineffective in this challenging setting. To tackle this challenge, we present a novel technique called Unlearnable Clusters (UCs) to generate label-agnostic unlearnable examples with cluster-wise perturbations. Furthermore, we propose to leverage VisionandLanguage Pre-trained Models (VLPMs) like CLIP as the surrogate model to improve the transferability of the crafted UCs to diverse domains. We empirically verify the effectiveness of our proposed approach under a variety of settings with different datasets, target models, and even commercial platforms Microsoft Azure and Baidu PaddlePaddle.

We consider the problem of estimating a multivariate function $f_0$ of bounded variation (BV), from noisy observations $y_i = f_0(x_i) + z_i$ made at random design points $x_i \in \mathbb{R}^d$, $i=1,\ldots,n$. We study an estimator that forms the Voronoi diagram of the design points, and then solves an optimization problem that regularizes according to a certain discrete notion of total variation (TV): the sum of weighted absolute differences of parameters $\theta_i,\theta_j$ (which estimate the function values $f_0(x_i),f_0(x_j)$) at all neighboring cells $i,j$ in the Voronoi diagram. This is seen to be equivalent to a variational optimization problem that regularizes according to the usual continuum (measure-theoretic) notion of TV, once we restrict the domain to functions that are piecewise constant over the Voronoi diagram. The regression estimator under consideration hence performs (shrunken) local averaging over adaptively formed unions of Voronoi cells, and we refer to it as the Voronoigram, following the ideas in Koenker (2005), and drawing inspiration from Tukey's regressogram (Tukey, 1961). Our contributions in this paper span both the conceptual and theoretical frontiers: we discuss some of the unique properties of the Voronoigram in comparison to TV-regularized estimators that use other graph-based discretizations; we derive the asymptotic limit of the Voronoi TV functional; and we prove that the Voronoigram is minimax rate optimal (up to log factors) for estimating BV functions that are essentially bounded.