时间动作分割（TAS）旨在在长期未经修剪的动作序列中对作用进行分类和定位。随着深度学习的成功，出现了许多深入的行动分割模型。但是，很少有TAS仍然是一个具有挑战性的问题。这项研究提出了一个基于少数骨架的TA的有效框架，包括数据增强方法和改进的模型。此处介绍了基于运动插值的数据增强方法，以解决数据不足的问题，并可以通过合成动作序列大大增加样品数量。此外，我们将连接式时间分类（CTC）层与设计用于基于骨架的TA的网络以获得优化的模型。利用CTC可以增强预测和地面真理之间的时间一致性，并进一步改善细分段的分割结果指标。对公共和自我结构的数据集进行了广泛的实验，包括两个小规模数据集和一个大规模数据集，显示了两种建议方法在改善基于少数骨架的TAS任务的性能方面的有效性。

在过去的十年中，对象检测任务主要由大型公共数据集定义。但是，由于图像收集和标签效率低下，构建对象检测数据集无法扩展。此外，大多数标签仍处于边界框的形式，这些框与真实的人类视觉系统所提供的信息要少得多。在本文中，我们提出了一种合成对象中的图像的方法，该图像可以保留对象的详细功能而无需带来无关的信息。简而言之，给定一组包含目标对象的图像，我们的算法首先训练一个模型以找到对象的近似中心作为锚点，然后进行轮廓回归以估算其边界，最后将对象融合到新场景中。我们的结果表明，在合成的图像中，对象的边界与背景很好地融合在一起。实验还表明，SOTA分割模型与我们的合成数据很好地工作。

作为自然语言处理领域（NLP）领域的广泛研究，基于方面的情感分析（ABSA）是预测文本中相对于相应方面所表达的情感的任务。不幸的是，大多数语言缺乏足够的注释资源，因此越来越多的研究人员专注于跨语义方面的情感分析（XABSA）。但是，最近的研究仅集中于跨语性数据对准而不是模型对齐。为此，我们提出了一个新颖的框架CL-XABSA：基于跨语言的情感分析的对比度学习。基于对比度学习，我们在不同的语义空间中关闭具有相同标签的样品之间的距离，从而实现了不同语言的语义空间的收敛。具体而言，我们设计了两种对比策略，即代币嵌入（TL-CTE）和情感水平的对比度学习，对代币嵌入（SL-CTE）的对比度学习，以使源语言和目标语言的语义空间正规化，以使其更加统一。由于我们的框架可以在培训期间以多种语言接收数据集，因此我们的框架不仅可以适应XABSA任务，而且可以针对基于多语言的情感分析（MABSA）进行调整。为了进一步提高模型的性能，我们执行知识蒸馏技术利用未标记的目标语言的数据。在蒸馏XABSA任务中，我们进一步探讨了不同数据（源数据集，翻译数据集和代码切换数据集）的比较有效性。结果表明，所提出的方法在XABSA，蒸馏XABSA和MABSA的三个任务中具有一定的改进。为了获得可重复性，我们的本文代码可在https://github.com/gklmip/cl-xabsa上获得。

作为世界上第四大语言家庭，Dravidian语言已成为自然语言处理（NLP）的研究热点。虽然Dravidian语言包含大量语言，但有相对较少的公众可用资源。此外，文本分类任务是自然语言处理的基本任务，如何将其与Dravidian语言中的多种语言相结合，仍然是Dravidian自然语言处理的主要困难。因此，为了解决这些问题，我们为Dravidian语言提出了一个多语言文本分类框架。一方面，该框架使用Labse预先训练的模型作为基础模型。针对多任务学习中文本信息偏见的问题，我们建议使用MLM策略选择语言特定的单词，并使用对抗训练来扰乱它们。另一方面，鉴于模型无法识别和利用语言之间的相关性的问题，我们进一步提出了一种特定于语言的表示模块，以丰富模型的语义信息。实验结果表明，我们提出的框架在多语言文本分类任务中具有重要性能，每个策略实现某些改进。

Ramp merging is a typical application of cooperative intelligent transportation system (C-ITS). Vehicle trajectories perceived by roadside sensors are importation complement to the limited visual field of on-board perception. Vehicle tracking and trajectory denoising algorithm is proposed in this paper to take full advantage of roadside cameras for vehicle trajectory and speed profile estimation. Dynamic speed guidance algorithm is proposed to help on-ramp vehicles to merge into mainline smoothly, even in non-cooperative environment where mainline vehicles are not expected to slow down to accommodate on-ramp vehicles. On-site experiments were taken out in a merging area of Hangzhou Belt Highway to testify our prototype system, and simulation analysis shows our proposed algorithm can achieve significant fuel savings during the ramp merging process.

Most regularized tensor regression research focuses on tensors predictors with scalars responses or vectors predictors to tensors responses. We consider the sparse low rank tensor on tensor regression where predictors $\mathcal{X}$ and responses $\mathcal{Y}$ are both high-dimensional tensors. By demonstrating that the general inner product or the contracted product on a unit rank tensor can be decomposed into standard inner products and outer products, the problem can be simply transformed into a tensor to scalar regression followed by a tensor decomposition. So we propose a fast solution based on stagewise search composed by contraction part and generation part which are optimized alternatively. We successfully demonstrate our method can out perform current methods in terms of accuracy, predictors selection by effectively incorporating the structural information.

While pre-trained Chinese language models have demonstrated impressive performance on a wide range of NLP tasks, the Chinese Spell Checking (CSC) task remains a challenge. Previous research has explored using information such as glyphs and phonetics to improve the ability to distinguish misspelled characters, with good results. However, the generalization ability of these models is not well understood: it is unclear whether they incorporate glyph-phonetic information and, if so, whether this information is fully utilized. In this paper, we aim to better understand the role of glyph-phonetic information in the CSC task and suggest directions for improvement. Additionally, we propose a new, more challenging, and practical setting for testing the generalizability of CSC models. All code is made publicly available.

We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym environments from RDDL declerative description. The discrete time step evolution of variables in RDDL is described by conditional probability functions, which fits naturally into the Gym step scheme. Furthermore, since RDDL is a lifted description, the modification and scaling up of environments to support multiple entities and different configurations becomes trivial rather than a tedious process prone to errors. We hope that pyRDDLGym will serve as a new wind in the reinforcement learning community by enabling easy and rapid development of benchmarks due to the unique expressive power of RDDL. By providing explicit access to the model in the RDDL description, pyRDDLGym can also facilitate research on hybrid approaches for learning from interaction while leveraging model knowledge. We present the design and built-in examples of pyRDDLGym, and the additions made to the RDDL language that were incorporated into the framework.

The success of state-of-the-art deep neural networks heavily relies on the presence of large-scale labelled datasets, which are extremely expensive and time-consuming to annotate. This paper focuses on tackling semi-supervised part segmentation tasks by generating high-quality images with a pre-trained GAN and labelling the generated images with an automatic annotator. In particular, we formulate the annotator learning as a learning-to-learn problem. Given a pre-trained GAN, the annotator learns to label object parts in a set of randomly generated images such that a part segmentation model trained on these synthetic images with their predicted labels obtains low segmentation error on a small validation set of manually labelled images. We further reduce this nested-loop optimization problem to a simple gradient matching problem and efficiently solve it with an iterative algorithm. We show that our method can learn annotators from a broad range of labelled images including real images, generated images, and even analytically rendered images. Our method is evaluated with semi-supervised part segmentation tasks and significantly outperforms other semi-supervised competitors when the amount of labelled examples is extremely limited.

In recent years, generative adversarial networks (GANs) have been an actively studied topic and shown to successfully produce high-quality realistic images in various domains. The controllable synthesis ability of GAN generators suggests that they maintain informative, disentangled, and explainable image representations, but leveraging and transferring their representations to downstream tasks is largely unexplored. In this paper, we propose to distill knowledge from GAN generators by squeezing and spanning their representations. We squeeze the generator features into representations that are invariant to semantic-preserving transformations through a network before they are distilled into the student network. We span the distilled representation of the synthetic domain to the real domain by also using real training data to remedy the mode collapse of GANs and boost the student network performance in a real domain. Experiments justify the efficacy of our method and reveal its great significance in self-supervised representation learning. Code is available at https://github.com/yangyu12/squeeze-and-span.