多人歧义是普通话素至光（G2P）转换中最关键的任务。先前的研究使用预先训练的语言模型，限制输出以及词性标签（POS）标记的额外信息来解决此问题。受这些策略的启发，我们提出了一种名为G2PW的新颖方法，该方法适应了可学习的软磁体重，以使用感兴趣的多音特征及其POS标记来调节BERT的输出。我们的实验并没有像以前的作品那样使用硬面膜，而是表明，学习候选音素的软加权功能会使性能受益。此外，我们提出的G2PW不需要额外的预训练POS标签模型，而将POS标签用作辅助功能，因为我们与统一的编码器同时训练POS标记模型。实验结果表明，我们的G2PW优于公共CPP数据集上的现有方法。所有代码，模型权重和用户友好的软件包均可公开使用。

现场文本识别（STR）已广泛研究学术界和工业。培训文本识别模型通常需要大量标记数据，但数据标签可能是困难，昂贵的或耗时的，尤其是对于传统的中国文本识别。据我们所知，缺乏传统文本认可的公共数据集。本文介绍了传统的中国合成数据引擎的框架，旨在提高文本识别模型性能。我们生成超过2000万遍的合成数据，并在7,000多个手动标记的数据TC-STR 7K-Word中收集为基准。实验结果表明，文本识别模型可以通过从头划痕与我们产生的合成数据或通过TC-STR 7K字进行进一步微调来实现更好的准确性。

由于最近的自然语言处理的进步，几种作品已经将伯特的预先接受审查的屏蔽语言模型（MLM）应用于语音识别的后校正。然而，现有的预先训练的模型仅考虑语义校正，同时忽略了单词的语音特征。因此，语义后校正将降低性能，因为在中国ASR中同音误差相当常见。在本文中，我们提出了一种集体利用了语境化表示的新方法以及错误与其替换候选人之间的语音信息来缓解中国ASR的错误率。我们对现实世界语音识别数据集的实验结果表明，我们所提出的方法明显地低于基线模型的CER，其利用预先训练的BERT MLM作为校正器。

尽管神经辐射场（NERF）在新型视图合成方面表现出了令人印象深刻的进步，但大多数方法通常需要具有准确的相机姿势的同一场景的多个输入图像。在这项工作中，我们试图将输入实质上减少到单个未予以的图像。现有的方法在本地图像功能上有条件重建一个3D对象，但通常会在远离源视图的视点处进行模糊的预测。为了解决这个问题，我们建议利用全球和本地功能形成表现力的3D表示。全局功能是从视觉变压器中学到的，而本地功能则从2D卷积网络中提取。为了综合一种新型视图，我们训练以学习的3D表示条件进行量渲染的多层感知器（MLP）网络。这种新颖的3D表示允许网络重建看不见的区域，而无需执行对称或规范坐标系等约束。我们的方法只能从单个输入图像中渲染新视图，并使用单个模型在多个对象类别中概括。定量和定性评估表明，所提出的方法可实现最先进的绩效，并使细节比现有方法更丰富。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.