Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.

In natural language processing (NLP), the context of a word or sentence plays an essential role. Contextual information such as the semantic representation of a passage or historical dialogue forms an essential part of a conversation and a precise understanding of the present phrase or sentence. However, the standard attention mechanisms typically generate weights using query and key but ignore context, forming a Bi-Attention framework, despite their great success in modeling sequence alignment. This Bi-Attention mechanism does not explicitly model the interactions between the contexts, queries and keys of target sequences, missing important contextual information and resulting in poor attention performance. Accordingly, a novel and general triple-attention (Tri-Attention) framework expands the standard Bi-Attention mechanism and explicitly interacts query, key, and context by incorporating context as the third dimension in calculating relevance scores. Four variants of Tri-Attention are generated by expanding the two-dimensional vector-based additive, dot-product, scaled dot-product, and bilinear operations in Bi-Attention to the tensor operations for Tri-Attention. Extensive experiments on three NLP tasks demonstrate that Tri-Attention outperforms about 30 state-of-the-art non-attention, standard Bi-Attention, contextual Bi-Attention approaches and pretrained neural language models1.

激光镜头和相机是两个用于自动驾驶中3D感知的互补传感器。激光点云具有准确的空间和几何信息，而RGB图像为上下文推理提供了纹理和颜色数据。为了共同利用激光雷达和相机，现有的融合方法倾向于基于校准，即一对一的映射，将每个3D点与一个投影图像像素对齐。但是，这些方法的性能高度依赖于校准质量，这对传感器的时间和空间同步敏感。因此，我们提出了一个动态的交叉注意（DCA）模块，具有新型的一对一的交叉模式映射，该模块从初始投影对邻域的最初投影中学习了多个偏移，从而发展了对校准误差的耐受性。此外，提出了A \ textIt {动态查询增强}来感知与模型无关的校准，从而进一步增强了DCA对初始未对准的耐受性。名为“动态跨注意网络”（DCAN）的整个融合体系结构利用了多级图像特征，并适应了点云的多个表示，这使DCA可以用作插件融合模块。对Nuscenes和Kitti的广泛实验证明了DCA的有效性。拟议的DCAN在Nuscenes检测挑战上优于最先进的方法。

及时，准确的土地使用映射是一个长期存在的问题，这对于有效的土地和太空规划和管理至关重要。由于复杂和混合的使用，直接从广泛使用的遥感图像（RSI）的准确土地使用映射方面，尤其是对于高密度城市而言。为了解决这个问题，在本文中，我们提出了一种基于粗到的机器学习的方法，用于包裹级城市土地使用映射，整合多源地理空间数据，包括RSI，利益点（POI）和区域 - 利益（AOI）数据。具体而言，我们首先根据公路网络产生的包裹将城市分为建立和非建造区域。然后，我们采用不同地区的包裹的不同分类策略，最后将分类结果组合到集成的土地使用图中。结果表明，所提出的方法可以显着超过基线方法，该方法将构建和非建造区域混合在一起，对于1级和2级分类，精度分别增加了25％和30％。此外，我们研究了很少探索的AOI数据，这可以将Level-1和Level-2分类精度提高13％和14％。这些结果证明了拟议方法的有效性，还表明了AOIS对土地使用映射的有用性，这对于进一步的研究很有价值。

在本文中，我们提出了一个迭代的自我训练框架，用于SIM到现实的6D对象姿势估计，以促进具有成本效益的机器人抓钩。给定bin选择场景，我们建立了一个光真实的模拟器来合成丰富的虚拟数据，并使用它来训练初始姿势估计网络。然后，该网络扮演教师模型的角色，该模型为未标记的真实数据生成了姿势预测。有了这些预测，我们进一步设计了一个全面的自适应选择方案，以区分可靠的结果，并将它们作为伪标签来更新学生模型以估算真实数据。为了不断提高伪标签的质量，我们通过将受过训练的学生模型作为新老师并使用精致的教师模型重新标记实际数据来迭代上述步骤。我们在公共基准和新发布的数据集上评估了我们的方法，分别提高了11.49％和22.62％的方法。我们的方法还能够将机器人箱的成功成功提高19.54％，这表明了对机器人应用的迭代SIM到现实解决方案的潜力。

我们呈现FURTIT，这是一种简单的3D形状分割网络的高效学习方法。FURTIT基于自我监督的任务，可以将3D形状的表面分解成几何基元。可以很容易地应用于用于3D形状分割的现有网络架构，并提高了几张拍摄设置中的性能，因为我们在广泛使用的ShapEnet和Partnet基准中展示。FISHIT在这种环境中优于现有的现有技术，表明对基元的分解是在学习对语义部分预测的陈述之前的有用。我们提出了许多实验，改变了几何基元和下游任务的选择，以证明该方法的有效性。

实现通用语言情报是自然语言处理的长期目标，标准评估基准发挥基本和指导作用。我们认为，对于通用语言智能评估，基准本身需要全面和系统。为此，我们提出了Cuge，一种中文语言理解和生成评估基准，具有以下特征：（1）分层基准框架，其中数据集主要选择和组织语言能力 - 任务数据集层次结构。 （2）多级评分策略，其中基于分层框架提供了不同级别的模型性能。为了促进CUGE，我们提供了一个公共排行榜，可以自定义，以支持灵活的模型判断标准。代表性预先训练的语言模型的评估结果表明了对通用语言智能的完善的充足空间。 Cuge在Cuge.baai.ac.cn上公开提供。

最近，通过单一或多个表示提出了许多方法，以提高点云语义分割的性能。但是，这些作品在性能，效率和记忆消耗中没有保持良好的平衡。为了解决这些问题，我们提出了Drinet ++，通过增强点云的点云与Voxel-Point原理来扩展Drinet。为了提高效率和性能，Drinet ++主要由两个模块组成：稀疏功能编码器和稀疏几何功能增强。稀疏特征编码器提取每个点的本地上下文信息，稀疏几何特征增强功能通过多尺度稀疏投影和细心的多尺度融合增强了稀疏点云​​的几何特性。此外，我们提出了在培训阶段的深度稀疏监督，以帮助收敛并减轻内存消耗问题。我们的Drinet ++在Semantickitti和Nuscenes数据集中实现了最先进的户外点云分段，同时运行得更快，更耗费较少的内存。

We aim to bridge the gap between our common-sense few-sample human learning and large-data machine learning. We derive a theory of human-like few-shot learning from von-Neuman-Landauer's principle. modelling human learning is difficult as how people learn varies from one to another. Under commonly accepted definitions, we prove that all human or animal few-shot learning, and major models including Free Energy Principle and Bayesian Program Learning that model such learning, approximate our theory, under Church-Turing thesis. We find that deep generative model like variational autoencoder (VAE) can be used to approximate our theory and perform significantly better than baseline models including deep neural networks, for image recognition, low resource language processing, and character recognition.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.