以移动为中心的AI应用程序对模型推断的资源效率有很高的要求。输入过滤是消除冗余以降低推理成本的有前途的方法。以前的努力已经针对许多应用程序量身定制了有效解决方案，但是尚未解决两个基本问题：（1）推理工作量的理论滤波器可指导输入过滤技术的应用，从而避免了资源受限的移动应用程序的试用成本; （2）功能嵌入的可辨别性可允许输入过滤对各种推理任务和输入内容有效。为了回答它们，我们首先将输入过滤问题正式化，理论上比较了推理模型和输入过滤器的假设复杂性，以了解优化潜力。然后，我们提出了第一个端到端可学习的输入过滤框架，该框架涵盖了大多数最先进的方法，并以可强大的可区分性嵌入功能。我们设计和实施支持六种输入方式和多个以移动为中心的部署的INFI。综合评估证实了我们的理论结果，并表明INFI在适用性，准确性和效率方面的表现优于强大的基准。 INFI获得8.5倍的吞吐量并节省95％的带宽，同时保持超过90％的精度，以用于移动平台上的视频分析应用程序。

现有的自我监督的单眼估计方法可以摆脱昂贵的注释并获得令人鼓舞的结果。但是，当直接采用接受固定分辨率训练的模型以评估其他不同决议时，这些方法会遭受严重的性能降解。在本文中，我们通过学习场景深度的规模不变性，提出了一个分辨率自适应自我监督的单眼估计方法（RA-DEPTH）。具体而言，我们提出了一种简单而有效的数据增强方法，以生成具有任意尺度的同一场景的图像。然后，我们开发了一个双重高分辨率网络，该网络使用具有密集交互的多路径编码器和解码器来汇总多尺度特征，以进行准确的深度推理。最后，为了明确了解场景深度的规模不变性，我们在具有不同尺度的深度预测上制定了跨尺度的深度一致性损失。对Kitti，Make3D和NYU-V2数据集进行了广泛的实验表明，RA-DEPTH不仅可以实现最新的性能，而且还表现出很好的解决能力。

视觉多层感知器（MLP）在计算机视觉任务中表现出了有希望的表现，并成为CNNS和Vision Transformers的主要竞争对手。他们使用令牌混合层来捕获交叉互动，而不是变形金刚使用的多头自我发项机制。然而，严重的参数化令牌混合层自然缺乏捕获局部信息和多粒性非本地关系的机制，因此它们的判别能力受到限制。为了解决这个问题，我们提出了一个新的位置空间门控单元（POSGU）。它利用经典相对位置编码（RPE）中使用的注意力公式，以有效地编码令牌混合的交叉关系。它可以成功地将视觉MLP的当前二次参数复杂度$ O（n^2）$ $ O（n^2）$ o（n）$（n）$和$ o（1）$。我们实验了两种RPE机制，并进一步提出了一个小组扩展，以实现多种环境的成就，以提高其表现力。然后，它们是一种新型视觉MLP的关键构建块，称为POSMLP。我们通过进行彻底的实验来评估所提出的方法的有效性，证明参数复杂性的提高或可比性能得到了改善或可比性。例如，对于在ImagEnet1k上训练的模型，我们实现了从72.14 \％\％\％\％的绩效提高，并且可学习的参数从$ 194M $ $ $ $ $ $ $ $ 1.182亿美元。代码可以在\ href {https://github.com/zhicaiwww/posmlp} {https://github.com/zhicaiwww/posmlp}中找到代码。

组合来自多视图图像的信息对于提高自动化方法的疾病诊断方法的性能和鲁棒性至关重要。但是，由于多视图图像的非对齐特性，跨视图的构建相关性和数据融合在很大程度上仍然是一个开放的问题。在这项研究中，我们提出了输血，这是一种基于变压器的体系结构，可使用卷积层和强大的注意机制合并不同的多视图成像信息。特别是，针对丰富的跨视图上下文建模和语义依赖性挖掘，提出了发散的融合注意（DIFA）模块，以解决从不同图像视图中捕获未对齐数据之间的长期相关性的关键问题。我们进一步提出了多尺度注意（MSA），以收集多尺度特征表示的全局对应关系。我们评估了心脏MRI（M \＆MS-2）挑战队列中多疾病，多视图\＆多中心右心室分段的输血。输血表明了针对最先进方法的领先绩效，并为多视图成像集成的新观点打开了稳健的医学图像分割。

自上而下的实例分割框架与自下而上的框架相比，它在对象检测方面表现出了优越性。虽然它有效地解决了过度细分，但自上而下的实例分割却遭受了过度处理问题。然而，完整的分割掩模对于生物图像分析至关重要，因为它具有重要的形态特性，例如形状和体积。在本文中，我们提出了一个区域建议纠正（RPR）模块，以解决这个具有挑战性的分割问题。特别是，我们提供了一个渐进式皇家模块，以逐渐将邻居信息引入一系列ROI。 ROI功能被馈入专门的进料网络（FFN）以进行提案框回归。有了其他邻居信息，提出的RPR模块显示了区域建议位置的校正显着改善，因此与最先进的基线方法相比，在三个生物图像数据集上表现出有利的实例分割性能。实验结果表明，所提出的RPR模块在基于锚固的和无锚的自上而下实例分割方法中有效，这表明该方法可以应用于生物学图像的一般自上而下实例分割。代码可用。

Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods. In the literature, however, most refinements are either briefly mentioned as implementation details or only visible in source code. In this paper, we will examine a collection of such refinements and empirically evaluate their impact on the final model accuracy through ablation study. We will show that, by combining these refinements together, we are able to improve various CNN models significantly. For example, we raise ResNet-50's top-1 validation accuracy from 75.3% to 79.29% on ImageNet. We will also demonstrate that improvement on image classification accuracy leads to better transfer learning performance in other application domains such as object detection and semantic segmentation.

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.

Text clustering and topic extraction are two important tasks in text mining. Usually, these two tasks are performed separately. For topic extraction to facilitate clustering, we can first project texts into a topic space and then perform a clustering algorithm to obtain clusters. To promote topic extraction by clustering, we can first obtain clusters with a clustering algorithm and then extract cluster-specific topics. However, this naive strategy ignores the fact that text clustering and topic extraction are strongly correlated and follow a chicken-and-egg relationship. Performing them separately fails to make them mutually benefit each other to achieve the best overall performance. In this paper, we propose an unsupervised text clustering and topic extraction framework (ClusTop) which integrates text clustering and topic extraction into a unified framework and can achieve high-quality clustering result and extract topics from each cluster simultaneously. Our framework includes four components: enhanced language model training, dimensionality reduction, clustering and topic extraction, where the enhanced language model can be viewed as a bridge between clustering and topic extraction. On one hand, it provides text embeddings with a strong cluster structure which facilitates effective text clustering; on the other hand, it pays high attention on the topic related words for topic extraction because of its self-attention architecture. Moreover, the training of enhanced language model is unsupervised. Experiments on two datasets demonstrate the effectiveness of our framework and provide benchmarks for different model combinations in this framework.

This paper illustrates the technologies of user next intent prediction with a concept knowledge graph. The system has been deployed on the Web at Alipay, serving more than 100 million daily active users. Specifically, we propose AlipayKG to explicitly characterize user intent, which is an offline concept knowledge graph in the Life-Service domain modeling the historical behaviors of users, the rich content interacted by users and the relations between them. We further introduce a Transformer-based model which integrates expert rules from the knowledge graph to infer the online user's next intent. Experimental results demonstrate that the proposed system can effectively enhance the performance of the downstream tasks while retaining explainability.