Tensor program tuning is a non-convex objective optimization problem, to which search-based approaches have proven to be effective. At the core of the search-based approaches lies the design of the cost model. Though deep learning-based cost models perform significantly better than other methods, they still fall short and suffer from the following problems. First, their feature extraction heavily relies on expert-level domain knowledge in hardware architectures. Even so, the extracted features are often unsatisfactory and require separate considerations for CPUs and GPUs. Second, a cost model trained on one hardware platform usually performs poorly on another, a problem we call cross-hardware unavailability. In order to address these problems, we propose TLP and MTLTLP. TLP is a deep learning-based cost model that facilitates tensor program tuning. Instead of extracting features from the tensor program itself, TLP extracts features from the schedule primitives. We treat schedule primitives as tensor languages. TLP is thus a Tensor Language Processing task. In this way, the task of predicting the tensor program latency through the cost model is transformed into a natural language processing (NLP) regression task. MTL-TLP combines Multi-Task Learning and TLP to cope with the cross-hardware unavailability problem. We incorporate these techniques into the Ansor framework and conduct detailed experiments. Results show that TLP can speed up the average search time by 9.1X and 3.0X on CPU and GPU workloads, respectively, compared to the state-of-the-art implementation. MTL-TLP can achieve a speed-up of 4.7X and 2.9X on CPU and GPU workloads, respectively, using only 7% of the target hardware data.

视频实例分割旨在预测每个帧的对象分割掩码，并关联多个帧的实例。最近的端到端视频实例分割方法能够在直接并行序列解码/预测框架中共同执行对象分割和实例关联。尽管这些方法通常可以预测较高质量的对象分割掩码，但它们可能无法在具有挑战性的情况下与实例相关联，因为它们没有明确对相邻帧的时间实例一致性进行建模。我们提出了一个一致的端到端视频实例分割框架，并在框架间反复注意，以建模相邻帧的时间实例一致性和全局时间上下文。我们的广泛实验表明，框架间的重复注意显着提高了时间实例的一致性，同时保持对象分割掩模的质量。我们的模型在YouTubevis-2019（62.1 \％）和YouTubevis-2021（54.7 \％）数据集上都达到了最新的精度。此外，定量和定性结果表明，所提出的方法可以预测更具时间一致的实例分割掩码。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

作为世界各地的Covid-19大流行横冲直撞，对视频会议激增的需求。为此，实时肖像分割成为一种流行的功能，以取代会议参与者的背景。虽然为从生命场景中提取身体姿势的分段提供了具有丰富的数据集，模型和算法，但纵向分割尚未在视频会议上下文中覆盖很好。为了促进该领域的进步，我们介绍了名为PP-Humanseg的开源解决方案。这项工作是第一个构建一个大型视频纵向数据集，其中包含291个会议场景中的291个视频，其中14K细微的帧和扩展到多摄像头电话。此外，我们提出了一种用于语义分割的新型语义连接感知学习（SCL），其引入了语义连接感知丢失，以提高来自连接的角度的分段结果。我们提出了一种超轻量级模型，具有SCL的实际肖像分割，实现IOO之间的最佳权衡和推理的速度。我们数据集的广泛评估展示了SCL和我们的模型的优越性。源代码可在https://github.com/paddlepaddle/paddleseg上获得。

未经监督的域名自适应人员重新识别（Reid）已被广泛调查以减轻域间隙的不利影响。这些作品假设目标域数据可以一次访问。然而，对于真实世界的流数据，这会阻碍及时适应改变数据统计数据以及对增加样本的充分利用。在本文中，为了解决更实际的情况，我们提出了一项新任务，终身无监督域自适应（Luda）人Reid。这是具有挑战性的，因为它要求模型不断适应目标环境的未标记数据，同时减轻灾难性的遗忘，为这么细粒度的检索任务。我们为这项任务设计了一个有效的计划，被称为Cluda-Reid，在那里反忘记与适应协调。具体地，提出了基于元的协调数据重放策略来重播旧数据并以协调的优化方向更新网络，以便适应和记忆。此外，我们提出了符合基于检索的任务的目标的旧知识蒸馏/继承的关系一致性学习。我们设置了两个评估设置来模拟实际应用方案。广泛的实验展示了我们Cluda-Reid与具有动态目标流的静止目标流和场景的方案的有效性。

多摄像机跟踪系统在需要高质量跟踪结果的应用中获得普及，例如摩擦结账，因为单眼多物体跟踪（MOT）系统由于闭塞而在杂乱和拥挤的环境中经常失败。通过恢复部分3D信息，多个高度重叠的相机可以显着减轻问题。但是，使用不同的相机设置和背景创建高质量多摄像头跟踪数据集的成本在该域中的数据集比例限制了数据集尺度。在本文中，我们在自动注释系统的帮助下提供了五种不同环境的大型密集标记的多摄像头跟踪数据集。该系统使用重叠和校准的深度和RGB相机来构建高性能3D跟踪器，可自动生成3D跟踪结果。使用摄像机参数将3D跟踪结果投影到每个RGB摄像头视图以创建2D跟踪结果。然后，我们手动检查并更正3D跟踪结果以确保标签质量，比完全手动注释便宜得多。我们使用两个实时多相机跟踪器和具有不同设置的人重新识别（REID）模型进行了广泛的实验。该数据集在杂乱和拥挤的环境中提供了更可靠的多摄像头，多目标跟踪系统的基准。此外，我们的结果表明，在此数据集中调整跟踪器和REID模型显着提高了它们的性能。我们的数据集将在接受这项工作后公开发布。

A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns. Although significant advances have been made to overcome the challenges in causal effect estimation with observational data, such as missing counterfactual outcomes and selection bias between treatment and control groups, the existing methods mainly focus on source-specific and stationary observational data. Such learning strategies assume that all observational data are already available during the training phase and from only one source. This practical concern of accessibility is ubiquitous in various academic and industrial applications. That's what it boiled down to: in the era of big data, we face new challenges in causal inference with observational data, i.e., the extensibility for incrementally available observational data, the adaptability for extra domain adaptation problem except for the imbalance between treatment and control groups, and the accessibility for an enormous amount of data. In this position paper, we formally define the problem of continual treatment effect estimation, describe its research challenges, and then present possible solutions to this problem. Moreover, we will discuss future research directions on this topic.

In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the content details and the style features. To stylize the image with sufficient style patterns, the content details may be damaged and sometimes the objects of images can not be distinguished clearly. For this reason, we present a new transformer-based method named STT for image style transfer and an edge loss which can enhance the content details apparently to avoid generating blurred results for excessive rendering on style features. Qualitative and quantitative experiments demonstrate that STT achieves comparable performance to state-of-the-art image style transfer methods while alleviating the content leak problem.

In recent years, the Transformer architecture has shown its superiority in the video-based person re-identification task. Inspired by video representation learning, these methods mainly focus on designing modules to extract informative spatial and temporal features. However, they are still limited in extracting local attributes and global identity information, which are critical for the person re-identification task. In this paper, we propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules to address the above issue. Specifically, MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips, respectively, achieving the holistic perception of the input person. We combine the outputs of all the stages for the final identification. In practice, to save the computational cost, the Spatial-Temporal Aggregation (STA) modules are first adopted in each stage to conduct the self-attention operations along the spatial and temporal dimensions separately. We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages. All of them are realized by employing newly designed self-attention operations with specific meanings. Moreover, temporal patch shuffling is also introduced to further improve the robustness of the model. Extensive experimental results demonstrate the effectiveness of the proposed modules in extracting the informative and discriminative information from the videos, and illustrate the MSTAT can achieve state-of-the-art accuracies on various standard benchmarks.

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.