As more and more artificial intelligence (AI) technologies move from the laboratory to real-world applications, the open-set and robustness challenges brought by data from the real world have received increasing attention. Data augmentation is a widely used method to improve model performance, and some recent works have also confirmed its positive effect on the robustness of AI models. However, most of the existing data augmentation methods are heuristic, lacking the exploration of their internal mechanisms. We apply the explainable artificial intelligence (XAI) method, explore the internal mechanisms of popular data augmentation methods, analyze the relationship between game interactions and some widely used robustness metrics, and propose a new proxy for model robustness in the open-set environment. Based on the analysis of the internal mechanisms, we develop a mask-based boosting method for data augmentation that comprehensively improves several robustness measures of AI models and beats state-of-the-art data augmentation approaches. Experiments show that our method can be widely applied to many popular data augmentation methods. Different from the adversarial training, our boosting method not only significantly improves the robustness of models, but also improves the accuracy of test sets. Our code is available at \url{https://github.com/Anonymous_for_submission}.

Deep metric learning aims to learn an embedding space, where semantically similar samples are close together and dissimilar ones are repelled against. To explore more hard and informative training signals for augmentation and generalization, recent methods focus on generating synthetic samples to boost metric learning losses. However, these methods just use the deterministic and class-independent generations (e.g., simple linear interpolation), which only can cover the limited part of distribution spaces around original samples. They have overlooked the wide characteristic changes of different classes and can not model abundant intra-class variations for generations. Therefore, generated samples not only lack rich semantics within the certain class, but also might be noisy signals to disturb training. In this paper, we propose a novel intra-class adaptive augmentation (IAA) framework for deep metric learning. We reasonably estimate intra-class variations for every class and generate adaptive synthetic samples to support hard samples mining and boost metric learning losses. Further, for most datasets that have a few samples within the class, we propose the neighbor correction to revise the inaccurate estimations, according to our correlation discovery where similar classes generally have similar variation distributions. Extensive experiments on five benchmarks show our method significantly improves and outperforms the state-of-the-art methods on retrieval performances by 3%-6%. Our code is available at https://github.com/darkpromise98/IAA

本文是第一个提供全面的系统设计概述以及融合方法选择标准的现实世界合作自动驾驶系统的选择标准，该标准为基础架构增强自主驾驶或IAAD。我们在路边和车辆侧计算和通信平台上介绍了IAAD硬件和软件的深入介绍。我们在现实部署方案的背景下广泛地表征了IAAD系统，并观察到沿着道路波动的网络状况是目前是合作自动驾驶的主要技术障碍。为了应对这一挑战，我们提出了新的融合方法，称为“框架间融合”和“计划融合”，以补充当前最新的“框架内融合”。我们证明，每种融合方法都有其自身的好处和约束。

随着可解释的人工智能（XAI）的快速发展，过去的一系列工作表明，基于扰动后的HOC XAI模型中对分布外（OOD）问题的担忧和解释在社会上是错误对准的。我们探讨了使用近似值来模仿黑盒模型的行为的事后解释方法的局限性。然后，我们提出了基于解释的反事实再培训（XCR），提取迅速提取的特征。 XCR应用了XAI模型生成的解释作为反事实输入，以重新培训黑框模型来解决OOD和社会错位问题。对流行图像数据集的评估表明，XCR只能保留12.5％的最关键功能而不更改黑框模型结构时，可以改善模型性能。此外，对腐败数据集基准的评估表明，XCR对改善模型鲁棒性非常有帮助，并积极影响OOD问题的校准。即使没有像某些OOD校准方法那样在验证集中进行校准，但损坏的数据度量标准的表现优于现有方法。如果应用了验证集上的校准，我们的方法还可以在OOD校准度量上使用当前的OOD校准方法。

在本文中，我们呈现了UFFORER，一种用于图像恢复的有效和高效的变换器架构，其中我们使用变压器块构建分层编码器解码器网络。在UFFAR中，有两个核心设计。首先，我们介绍了一个新颖的本地增强型窗口（Lewin）变压器块，其执行基于窗口的自我关注而不是全局自我关注。它显着降低了高分辨率特征映射的计算复杂性，同时捕获本地上下文。其次，我们提出了一种以多尺度空间偏置的形式提出了一种学习的多尺度恢复调制器，以调整UFFORER解码器的多个层中的特征。我们的调制器展示了卓越的能力，用于恢复各种图像恢复任务的详细信息，同时引入边缘额外参数和计算成本。通过这两个设计提供支持，UFFORER享有高能力，可以捕获本地和全局依赖性的图像恢复。为了评估我们的方法，在几种图像恢复任务中进行了广泛的实验，包括图像去噪，运动脱棕，散焦和污染物。没有钟声和口哨，与最先进的算法相比，我们的UFormer实现了卓越的性能或相当的性能。代码和模型可在https://github.com/zhendongwang6/uformer中找到。

Scene text spotting is of great importance to the computer vision community due to its wide variety of applications. Recent methods attempt to introduce linguistic knowledge for challenging recognition rather than pure visual classification. However, how to effectively model the linguistic rules in end-to-end deep networks remains a research challenge. In this paper, we argue that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input. Correspondingly, we propose an autonomous, bidirectional and iterative ABINet++ for scene text spotting. Firstly, the autonomous suggests enforcing explicitly language modeling by decoupling the recognizer into vision model and language model and blocking gradient flow between both models. Secondly, a novel bidirectional cloze network (BCN) as the language model is proposed based on bidirectional feature representation. Thirdly, we propose an execution manner of iterative correction for the language model which can effectively alleviate the impact of noise input. Finally, to polish ABINet++ in long text recognition, we propose to aggregate horizontal features by embedding Transformer units inside a U-Net, and design a position and content attention module which integrates character order and content to attend to character features precisely. ABINet++ achieves state-of-the-art performance on both scene text recognition and scene text spotting benchmarks, which consistently demonstrates the superiority of our method in various environments especially on low-quality images. Besides, extensive experiments including in English and Chinese also prove that, a text spotter that incorporates our language modeling method can significantly improve its performance both in accuracy and speed compared with commonly used attention-based recognizers.

In recent years, machine learning has achieved impressive results across different application areas. However, machine learning algorithms do not necessarily perform well on a new domain with a different distribution than its training set. Domain Adaptation (DA) is used to mitigate this problem. One approach of existing DA algorithms is to find domain invariant features whose distributions in the source domain are the same as their distribution in the target domain. In this paper, we propose to let the classifier that performs the final classification task on the target domain learn implicitly the invariant features to perform classification. It is achieved via feeding the classifier during training generated fake samples that are similar to samples from both the source and target domains. We call these generated samples domain-agnostic samples. To accomplish this we propose a novel variation of generative adversarial networks (GAN), called the MiddleGAN, that generates fake samples that are similar to samples from both the source and target domains, using two discriminators and one generator. We extend the theory of GAN to show that there exist optimal solutions for the parameters of the two discriminators and one generator in MiddleGAN, and empirically show that the samples generated by the MiddleGAN are similar to both samples from the source domain and samples from the target domain. We conducted extensive evaluations using 24 benchmarks; on the 24 benchmarks, we compare MiddleGAN against various state-of-the-art algorithms and outperform the state-of-the-art by up to 20.1\% on certain benchmarks.

对卷积神经网络（CNN）的知识蒸馏（KD）进行了广泛的研究，以提高小型模型的性能。最近，Vision Transformer（VIT）在许多计算机视觉任务上取得了巨大的成功，而VIT的KD也需要实现。但是，除了基于输出logit的KD之外，由于巨大的结构间隙，其他基于特征的CNN基于特征的KD方法不能直接应用于VIT。在本文中，我们探讨了对VIT的基于特征的蒸馏方式。根据VIT中特征地图的性质，我们设计了一系列受控的实验，并为VIT特征蒸馏提供了三个实用指南。我们的一些发现甚至与CNN时代的实践相反。根据三个准则，我们提出了基于功能的方法Vitkd，从而为学生带来一致且相当大的改进。在ImagEnet-1K上，我们将DEIT微型从74.42％提高到76.06％，Deit-Small从80.55％提高到81.95％，而Deit-Base则从81.76％升至83.46％。此外，Vitkd和基于Logit的KD方法是互补的，可以直接使用。这种组合可以进一步提高学生的表现。具体而言，学生DEIT微小，小和基础分别达到77.78％，83.59％和85.41％。该代码可在https://github.com/yzd-v/cls_kd上找到。

文本到图像生成旨在生成与给定文本一致的真实图像。先前的作品主要通过堆叠生成器 - 歧义器对进行多个对抗训练，主要采用多阶段体系结构，在该培训中，用于提供发电指导的文本语义在所有阶段都保持静态。这项工作认为，每个阶段的文本特征应根据历史阶段的状态（即历史阶段的文本和图像特征）进行自适应重新组合，以在粗到精细的生成过程中提供多样化和准确的语义指导。因此，我们提出了一种新颖的动力学语义演化gan（DSE-GAN），以在新颖的单一对抗性多阶段体系结构下重新构成每个阶段的文本特征。具体而言，我们设计（1）动态语义演化（DSE）模块，该模块首先汇总了历史图像特征以总结生成反馈，然后动态选择在每个阶段重新组装的单词，并通过动态地组装它们增强或抑制不同的粒度子空间的语义。 （2）单个对抗性多阶段体系结构（SAMA），通过消除复杂的多个对抗训练要求扩展了先前的结构，因此可以允许更多的文本图像相互作用阶段，并最终促进DSE模块。我们进行了全面的实验，并表明DSE-GAN在两个广泛使用的基准分别（即CUB-200和MSCOCO）上获得了7.48 \％和37.8％的相对FID。

由于非平稳性，现实世界多变量时间序列（MTS）的分布会随着时间而变化，称为分布漂移。大多数现有的MT预测模型都会极大地遭受分销漂移的影响，并随着时间的推移降低了预测性能。现有方法通过适应最新到达数据或根据未来数据得出的元知识进行自我纠正来解决分布漂移。尽管在MT的预测中取得了巨大的成功，但这些方法几乎无法捕获固有的分布变化，尤其是从分布的角度来看。因此，我们提出了一个新型的框架时间条件变化自动编码器（TCVAE），以对MTS中历史观察结果和未来数据之间的动态分布依赖性进行建模，并将依赖性作为时间条件分布推断为利用潜在变量。具体而言，新型的颞鹰注意机制代表了随后馈入馈送前网络的时间因素，以估计潜在变量的先前高斯分布。时间因素的表示进一步动态地调整了基于变压器的编码器和解码器的结构，以利用门控注意机制来变化。此外，我们引入条件连续归一化流量，以将先前的高斯转化为复杂且无形式的分布，以促进对时间条件分布的灵活推断。在六个现实世界MTS数据集上进行的广泛实验表明，与最先进的MTS预测基线相比，TCVAE的出色鲁棒性和有效性。我们进一步说明了TCVAE通过多方面的案例研究和现实情况下的可视化来说明TCVAE的适用性。