How to effectively explore the colors of reference exemplars and propagate them to colorize each frame is vital for exemplar-based video colorization. In this paper, we present an effective BiSTNet to explore colors of reference exemplars and utilize them to help video colorization by a bidirectional temporal feature fusion with the guidance of semantic image prior. We first establish the semantic correspondence between each frame and the reference exemplars in deep feature space to explore color information from reference exemplars. Then, to better propagate the colors of reference exemplars into each frame and avoid the inaccurate matches colors from exemplars we develop a simple yet effective bidirectional temporal feature fusion module to better colorize each frame. We note that there usually exist color-bleeding artifacts around the boundaries of the important objects in videos. To overcome this problem, we further develop a mixed expert block to extract semantic information for modeling the object boundaries of frames so that the semantic image prior can better guide the colorization process for better performance. In addition, we develop a multi-scale recurrent block to progressively colorize frames in a coarse-to-fine manner. Extensive experimental results demonstrate that the proposed BiSTNet performs favorably against state-of-the-art methods on the benchmark datasets. Our code will be made available at \url{https://yyang181.github.io/BiSTNet/}

深度强化学习在基于激光的碰撞避免有效的情况下取得了巨大的成功，因为激光器可以感觉到准确的深度信息而无需太多冗余数据，这可以在算法从模拟环境迁移到现实世界时保持算法的稳健性。但是，高成本激光设备不仅很难为大型机器人部署，而且还表现出对复杂障碍的鲁棒性，包括不规则的障碍，例如桌子，桌子，椅子和架子，以及复杂的地面和特殊材料。在本文中，我们提出了一个新型的基于单眼相机的复杂障碍避免框架。特别是，我们创新地将捕获的RGB图像转换为伪激光测量，以进行有效的深度强化学习。与在一定高度捕获的传统激光测量相比，仅包含距离附近障碍的一维距离信息，我们提议的伪激光测量融合了捕获的RGB图像的深度和语义信息，这使我们的方法有效地有效障碍。我们还设计了一个功能提取引导模块，以加重输入伪激光测量，并且代理对当前状态具有更合理的关注，这有利于提高障碍避免政策的准确性和效率。

手语制作（SLP）旨在将口语语言自动转化为符号序列。 SLP的核心过程是将符号光泽序列转换为其相应的标志姿势序列（G2P）。大多数现有的G2P模型通常以自回归方式执行这种条件的远程生成，这不可避免地导致错误的积累。为了解决这个问题，我们提出了一种量化量子序列序列的生成的矢量量化扩散方法，称为poseVQ扩散，这是一种迭代性非自动入学方法。具体而言，我们首先引入量化量化变量自动编码器（姿势VQVAE）模型，以表示姿势序列作为一系列潜在代码。然后，我们通过最近开发的扩散体系结构的扩展来对潜在离散空间进行建模。为了更好地利用时空信息，我们介绍了一种新颖的体系结构，即CodeUnet，以在离散空间中生成更高质量的姿势序列。此外，利用学习的代码，我们开发了一种新型的顺序k-nearest-neighbours方法，以预测相应的光泽序列的姿势序列的可变长度。因此，与自回旋G2P模型相比，我们的模型具有更快的采样速度，并产生明显更好的结果。与以前的非自动入学G2P方法相比，PoseVQ扩散通过迭代改进改善了预测的结果，从而在SLP评估基准上获得了最新的结果。

在目标属性下设计和生成新数据一直吸引着各种关键应用，例如分子设计，图像编辑和语音合成。传统手工制作的方法在很大程度上依赖于专业知识经验和强化人类的努力，但仍遭受科学知识和低吞吐量的不足，无法支持有效，有效的数据生成。最近，深度学习的进步引起了可以学习数据的基本表示和属性的表达方法。这种能力为弄清数据的结构模式和功能特性之间的相互关系提供了新的机会，并利用这种关系以生成所需属性的结构数据。本文对这个有前途的研究领域进行了系统的综述，通常称为可控制的深度数据生成。首先，提出了潜在的挑战，并提供了初步的挑战。然后，正式定义了可控的深度数据生成，提出了各种技术的分类法，并总结了该特定领域中的评估指标。之后，引入了可控制的深度数据生成的令人兴奋的应用程序，并对现有的作品进行了实验分析和比较。最后，突出显示了可控制的深度数据生成的有希望的未来方向，并确定了五个潜在的挑战。

除局部相关性外，开放域的Factoid问题回答的段落排名还需要一个段落以包含答案（答案）。尽管最近的一些研究将一些阅读能力纳入了排名者以说明答复性，但排名仍然受到该领域通常可用的训练数据的嘈杂性质的阻碍，这将考虑任何包含答案实体作为正样本的段落。但是，段落中的答案实体不一定与给定的问题有关。为了解决该问题，我们提出了一种基于生成对抗性神经网络的通道重新管理的方法，称为\ ttt {pregan}，除了局部相关性外，还结合了关于答复性的歧视者。目的是强迫发电机对局部相关的段落进行排名，并包含答案。五个公共数据集的实验表明，\ ttt {pregan}可以更好地对适当的段落进行排名，从而提高质量检查系统的有效性，并在不使用外部数据的情况下优于现有方法。

在胸部X射线图像中定位疾病很少仔细注释可以节省大量的人类努力。最近的作品通过创新的弱监督算法（例如多稳定学习（MIL）和类激活图（CAM））处理了这项任务，但是，这些方法通常会产生不准确或不完整的区域。原因之一是忽视了每个图像内部解剖区域的关系中隐藏的病理意义以及跨图像的关系。在本文中，我们认为，作为上下文和补偿信息的跨区域和跨图像关系对于获得更一致和更一致的区域至关重要。为了建模关系，我们提出了图形正则嵌入网络（GREN），该网络（GREN）利用图像和图像间信息来定位胸部X射线图像上的疾病。 Gren使用预先训练的U-NET来分割肺裂片，然后使用图像内图形图对肺裂片之间的内图像进行建模以比较不同的区域。同时，内部图像之间的关系是通过图像间图建模的，以比较多个图像。此过程模仿了放射科医生的训练和决策过程：比较多个区域和图像进行诊断。为了使神经网络的深层嵌入层保留结构信息（在本地化任务中很重要），我们使用哈希编码和锤击距离来计算图形，这些图形用作正规化器来促进训练。通过这种情况，我们的方法实现了NIH胸部X射线数据集的最新结果，以实现弱监督疾病的定位。我们的代码可在线访问（https://github.com/qibaolian/gren）。

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.