Time-series anomaly detection is an important task and has been widely applied in the industry. Since manual data annotation is expensive and inefficient, most applications adopt unsupervised anomaly detection methods, but the results are usually sub-optimal and unsatisfactory to end customers. Weak supervision is a promising paradigm for obtaining considerable labels in a low-cost way, which enables the customers to label data by writing heuristic rules rather than annotating each instance individually. However, in the time-series domain, it is hard for people to write reasonable labeling functions as the time-series data is numerically continuous and difficult to be understood. In this paper, we propose a Label-Efficient Interactive Time-Series Anomaly Detection (LEIAD) system, which enables a user to improve the results of unsupervised anomaly detection by performing only a small amount of interactions with the system. To achieve this goal, the system integrates weak supervision and active learning collaboratively while generating labeling functions automatically using only a few labeled data. All of these techniques are complementary and can promote each other in a reinforced manner. We conduct experiments on three time-series anomaly detection datasets, demonstrating that the proposed system is superior to existing solutions in both weak supervision and active learning areas. Also, the system has been tested in a real scenario in industry to show its practicality.

Self-supervised representation learning follows a paradigm of withholding some part of the data and tasking the network to predict it from the remaining part. Towards this end, masking has emerged as a generic and powerful tool where content is withheld along the sequential dimension, e.g., spatial in images, temporal in audio, and syntactic in language. In this paper, we explore the orthogonal channel dimension for generic data augmentation. The data for each channel is quantized through a non-uniform quantizer, with the quantized value sampled randomly within randomly sampled quantization bins. From another perspective, quantization is analogous to channel-wise masking, as it removes the information within each bin, but preserves the information across bins. We apply the randomized quantization in conjunction with sequential augmentations on self-supervised contrastive models. This generic approach achieves results on par with modality-specific augmentation on vision tasks, and state-of-the-art results on 3D point clouds as well as on audio. We also demonstrate this method to be applicable for augmenting intermediate embeddings in a deep neural network on the comprehensive DABS benchmark which is comprised of various data modalities. Code is availabel at http://www.github.com/microsoft/random_quantize.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Vision transformers (ViTs) have achieved impressive results on various computer vision tasks in the last several years. In this work, we study the capability of frozen ViTs, pretrained only on visual data, to generalize to audio-visual data without finetuning any of its original parameters. To do so, we propose a latent audio-visual hybrid (LAVISH) adapter that adapts pretrained ViTs to audio-visual tasks by injecting a small number of trainable parameters into every layer of a frozen ViT. To efficiently fuse visual and audio cues, our LAVISH adapter uses a small set of latent tokens, which form an attention bottleneck, thus, eliminating the quadratic cost of standard cross-attention. Compared to the existing modality-specific audio-visual methods, our approach achieves competitive or even better performance on various audio-visual tasks while using fewer tunable parameters and without relying on costly audio pretraining or external audio encoders. Our code is available at https://genjib.github.io/project_page/LAVISH/

Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade. However, the outputs of the transition function in the existing GPSSMs are assumed to be independent, meaning that the GPSSMs cannot exploit the inductive biases between different outputs and lose certain model capacities. To address this issue, this paper proposes an output-dependent and more realistic GPSSM by utilizing the well-known, simple yet practical linear model of coregionalization (LMC) framework to represent the output dependency. To jointly learn the output-dependent GPSSM and infer the latent states, we propose a variational sparse GP-based learning method that only gently increases the computational complexity. Experiments on both synthetic and real datasets demonstrate the superiority of the output-dependent GPSSM in terms of learning and inference performance.

Localizing anatomical landmarks are important tasks in medical image analysis. However, the landmarks to be localized often lack prominent visual features. Their locations are elusive and easily confused with the background, and thus precise localization highly depends on the context formed by their surrounding areas. In addition, the required precision is usually higher than segmentation and object detection tasks. Therefore, localization has its unique challenges different from segmentation or detection. In this paper, we propose a zoom-in attentive network (ZIAN) for anatomical landmark localization in ocular images. First, a coarse-to-fine, or "zoom-in" strategy is utilized to learn the contextualized features in different scales. Then, an attentive fusion module is adopted to aggregate multi-scale features, which consists of 1) a co-attention network with a multiple regions-of-interest (ROIs) scheme that learns complementary features from the multiple ROIs, 2) an attention-based fusion module which integrates the multi-ROIs features and non-ROI features. We evaluated ZIAN on two open challenge tasks, i.e., the fovea localization in fundus images and scleral spur localization in AS-OCT images. Experiments show that ZIAN achieves promising performances and outperforms state-of-the-art localization methods. The source code and trained models of ZIAN are available at https://github.com/leixiaofeng-astar/OMIA9-ZIAN.

作为最成功的AI驱动应用程序之一，推荐系统的目的是通过在我们生活的许多方面提供个性化建议，以有效而有效的方式帮助人们做出适当的决定，尤其是针对各种面向人类的在线服务，例如E-商务平台和社交媒体网站。在过去的几十年中，推荐系统的快速发展通过创造经济价值，节省时间和精力以及促进社会利益，从而使人类受益匪浅。但是，最近的研究发现，数据驱动的推荐系统可能会对用户和社会构成严重威胁，例如传播虚假新闻以操纵社交媒体网站中的公众舆论，扩大不公平为代表性不足的团体或在工作匹配服务中的个人，或从建议结果中推断隐私信息。因此，系统的可信赖性一直吸引着各个方面的关注，以减轻推荐系统引起的负面影响，以增强公众对推荐系统技术的信任。在这项调查中，我们提供了可信赖的推荐系统（TREC）的全面概述，特别关注六个最重要的方面；即安全与鲁棒性，非歧视与公平，解释性，隐私，环境福祉以及问责制和可审计性。对于每个方面，我们总结了最近的相关技术，并讨论了潜在的研究方向，以帮助未来实现值得信赖的推荐系统。

在没有解密的情况下对加密数据进行神经网络推断是一种流行的方法，可以使隐私神经网络（PNET）作为服务。与用于机器学习的常规神经网络相比，PNET需要额外的编码，例如量化精确数字和多项式激活。加密输入还引入了新颖的挑战，例如对抗性鲁棒性和安全性。据我们所知，我们是第一个研究问题，包括（i）PNET是否比常规神经网络对对抗性输入更强大？ （ii）如何在没有解密的情况下设计强大的PNET？我们建议使用PNET攻击来生成黑框对抗示例，这些示例可以成功攻击目标和非目标方式。攻击结果表明，需要改进针对对抗输入的PNET鲁棒性。这不是一项琐碎的任务，因为PNET模型所有者无法访问输入值的明文，这阻止了现有检测和防御方法的应用，例如输入调整，模型归一化和对抗性培训。为了应对这一挑战，我们提出了一种新的快速准确的噪声插入方法，称为RPNET，以设计强大的私人神经网络。我们的综合实验表明，PNET-ITSTACK比先前的工作减少了至少$ 2.5 \ times $的查询。我们从理论上分析了我们的RPNET方法，并证明RPNET可以降低$ \ sim 91.88 \％$ $攻击成功率。

本文回顾了AIM 2022上压缩图像和视频超级分辨率的挑战。这项挑战包括两条曲目。轨道1的目标是压缩图像的超分辨率，轨迹〜2靶向压缩视频的超分辨率。在轨道1中，我们使用流行的数据集DIV2K作为培训，验证和测试集。在轨道2中，我们提出了LDV 3.0数据集，其中包含365个视频，包括LDV 2.0数据集（335个视频）和30个其他视频。在这一挑战中，有12支球队和2支球队分别提交了赛道1和赛道2的最终结果。所提出的方法和解决方案衡量了压缩图像和视频上超分辨率的最先进。提出的LDV 3.0数据集可在https://github.com/renyang-home/ldv_dataset上找到。此挑战的首页是在https://github.com/renyang-home/aim22_compresssr。

在现代制造环境中，对接触式任务的需求正在迅速增长。但是，很少有传统的机器人组装技能考虑任务执行过程中的环境限制，并且大多数人将这些限制作为终止条件。在这项研究中，我们提出了基于推动的混合位置/力组装技能，该技能可以在任务执行过程中最大化环境限制。据我们所知，这是在执行程序集任务期间使用推动操作考虑的第一项工作。我们已经证明，我们的技能可以使用移动操纵器系统组装任务实验最大化环境约束的利用，并在执行中实现100 \％的成功率。