作为智能机器人的一项基本任务，Visual Slam在过去几十年中取得了长足的进步。但是，在高度弱质地的环境下，强大的大满贯仍然非常具有挑战性。在本文中，我们提出了一个名为RWT-Slam的新型视觉大满贯系统，以解决这个问题。我们修改LOFTR网络，该网络能够在低纹理的场景下产生密集的点匹配以生成特征描述符。为了将新功能集成到流行的Orb-Slam框架中，我们开发了功能面具，以滤除不可靠的功能并采用KNN策略来增强匹配的鲁棒性。我们还对新的描述符进行了视觉词汇，以有效地循环结束。在TUM和Openloris等各种公共数据集以及我们自己的数据中测试了由此产生的RWT-SLAM。结果显示在高度弱质地的环境下表现非常有希望。

近年来，由于深度学习技术的发展，LiDar Point Clouds的3D对象检测取得了长足的进步。尽管基于体素或基于点的方法在3D对象检测中很受欢迎，但它们通常涉及耗时的操作，例如有关体素的3D卷积或点之间的球查询，从而使所得网络不适合时间关键应用程序。另一方面，基于2D视图的方法具有较高的计算效率，而通常比基于体素或基于点的方法获得的性能低。在这项工作中，我们提出了一个基于实时视图的单阶段3D对象检测器，即CVFNET完成此任务。为了在苛刻的效率条件下加强跨视图的学习，我们的框架提取了不同视图的特征，并以有效的渐进式方式融合了它们。我们首先提出了一个新颖的点范围特征融合模块，该模块在多个阶段深入整合点和范围视图特征。然后，当将所获得的深点视图转换为鸟类视图时，特殊的切片柱旨在很好地维护3D几何形状。为了更好地平衡样品比率，提出了一个稀疏的柱子检测头，将检测集中在非空网上。我们对流行的Kitti和Nuscenes基准进行了实验，并以准确性和速度来实现最先进的性能。

In contrast to the control-theoretic methods, the lack of stability guarantee remains a significant problem for model-free reinforcement learning (RL) methods. Jointly learning a policy and a Lyapunov function has recently become a promising approach to ensuring the whole system with a stability guarantee. However, the classical Lyapunov constraints researchers introduced cannot stabilize the system during the sampling-based optimization. Therefore, we propose the Adaptive Stability Certification (ASC), making the system reach sampling-based stability. Because the ASC condition can search for the optimal policy heuristically, we design the Adaptive Lyapunov-based Actor-Critic (ALAC) algorithm based on the ASC condition. Meanwhile, our algorithm avoids the optimization problem that a variety of constraints are coupled into the objective in current approaches. When evaluated on ten robotic tasks, our method achieves lower accumulated cost and fewer stability constraint violations than previous studies.

A storyboard is a roadmap for video creation which consists of shot-by-shot images to visualize key plots in a text synopsis. Creating video storyboards however remains challenging which not only requires association between high-level texts and images, but also demands for long-term reasoning to make transitions smooth across shots. In this paper, we propose a new task called Text synopsis to Video Storyboard (TeViS) which aims to retrieve an ordered sequence of images to visualize the text synopsis. We construct a MovieNet-TeViS benchmark based on the public MovieNet dataset. It contains 10K text synopses each paired with keyframes that are manually selected from corresponding movies by considering both relevance and cinematic coherence. We also present an encoder-decoder baseline for the task. The model uses a pretrained vision-and-language model to improve high-level text-image matching. To improve coherence in long-term shots, we further propose to pre-train the decoder on large-scale movie frames without text. Experimental results demonstrate that our proposed model significantly outperforms other models to create text-relevant and coherent storyboards. Nevertheless, there is still a large gap compared to human performance suggesting room for promising future work.

Retrieval-augmented in-context learning has emerged as a powerful approach for addressing knowledge-intensive tasks using frozen language models (LM) and retrieval models (RM). Existing work has combined these in simple "retrieve-then-read" pipelines in which the RM retrieves passages that are inserted into the LM prompt. To begin to fully realize the potential of frozen LMs and RMs, we propose Demonstrate-Search-Predict (DSP), a framework that relies on passing natural language texts in sophisticated pipelines between an LM and an RM. DSP can express high-level programs that bootstrap pipeline-aware demonstrations, search for relevant passages, and generate grounded predictions, systematically breaking down problems into small transformations that the LM and RM can handle more reliably. We have written novel DSP programs for answering questions in open-domain, multi-hop, and conversational settings, establishing in early evaluations new state-of-the-art in-context learning results and delivering 37-200%, 8-40%, and 80-290% relative gains against vanilla LMs, a standard retrieve-then-read pipeline, and a contemporaneous self-ask pipeline, respectively.

Future work sentences (FWS) are the particular sentences in academic papers that contain the author's description of their proposed follow-up research direction. This paper presents methods to automatically extract FWS from academic papers and classify them according to the different future directions embodied in the paper's content. FWS recognition methods will enable subsequent researchers to locate future work sentences more accurately and quickly and reduce the time and cost of acquiring the corpus. The current work on automatic identification of future work sentences is relatively small, and the existing research cannot accurately identify FWS from academic papers, and thus cannot conduct data mining on a large scale. Furthermore, there are many aspects to the content of future work, and the subdivision of the content is conducive to the analysis of specific development directions. In this paper, Nature Language Processing (NLP) is used as a case study, and FWS are extracted from academic papers and classified into different types. We manually build an annotated corpus with six different types of FWS. Then, automatic recognition and classification of FWS are implemented using machine learning models, and the performance of these models is compared based on the evaluation metrics. The results show that the Bernoulli Bayesian model has the best performance in the automatic recognition task, with the Macro F1 reaching 90.73%, and the SCIBERT model has the best performance in the automatic classification task, with the weighted average F1 reaching 72.63%. Finally, we extract keywords from FWS and gain a deep understanding of the key content described in FWS, and we also demonstrate that content determination in FWS will be reflected in the subsequent research work by measuring the similarity between future work sentences and the abstracts.

Heterogeneous graph contrastive learning has received wide attention recently. Some existing methods use meta-paths, which are sequences of object types that capture semantic relationships between objects, to construct contrastive views. However, most of them ignore the rich meta-path context information that describes how two objects are connected by meta-paths. On the other hand, they fail to distinguish hard negatives from false negatives, which could adversely affect the model performance. To address the problems, we propose MEOW, a heterogeneous graph contrastive learning model that considers both meta-path contexts and weighted negative samples. Specifically, MEOW constructs a coarse view and a fine-grained view for contrast. The former reflects which objects are connected by meta-paths, while the latter uses meta-path contexts and characterizes the details on how the objects are connected. We take node embeddings in the coarse view as anchors, and construct positive and negative samples from the fine-grained view. Further, to distinguish hard negatives from false negatives, we learn weights of negative samples based on node clustering. We also use prototypical contrastive learning to pull close embeddings of nodes in the same cluster. Finally, we conduct extensive experiments to show the superiority of MEOW against other state-of-the-art methods.

Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.

Video capture is the most extensively utilized human perception source due to its intuitively understandable nature. A desired video capture often requires multiple environmental conditions such as ample ambient-light, unobstructed space, and proper camera angle. In contrast, wireless measurements are more ubiquitous and have fewer environmental constraints. In this paper, we propose CSI2Video, a novel cross-modal method that leverages only WiFi signals from commercial devices and a source of human identity information to recover fine-grained surveillance video in a real-time manner. Specifically, two tailored deep neural networks are designed to conduct cross-modal mapping and video generation tasks respectively. We make use of an auto-encoder-based structure to extract pose features from WiFi frames. Afterward, both extracted pose features and identity information are merged to generate synthetic surveillance video. Our solution generates realistic surveillance videos without any expensive wireless equipment and has ubiquitous, cheap, and real-time characteristics.

Graph Neural Networks (GNNs), originally proposed for node classification, have also motivated many recent works on edge prediction (a.k.a., link prediction). However, existing methods lack elaborate design regarding the distinctions between two tasks that have been frequently overlooked: (i) edges only constitute the topology in the node classification task but can be used as both the topology and the supervisions (i.e., labels) in the edge prediction task; (ii) the node classification makes prediction over each individual node, while the edge prediction is determinated by each pair of nodes. To this end, we propose a novel edge prediction paradigm named Edge-aware Message PassIng neuRal nEtworks (EMPIRE). Concretely, we first introduce an edge splitting technique to specify use of each edge where each edge is solely used as either the topology or the supervision (named as topology edge or supervision edge). We then develop a new message passing mechanism that generates the messages to source nodes (through topology edges) being aware of target nodes (through supervision edges). In order to emphasize the differences between pairs connected by supervision edges and pairs unconnected, we further weight the messages to highlight the relative ones that can reflect the differences. In addition, we design a novel negative node-pair sampling trick that efficiently samples 'hard' negative instances in the supervision instances, and can significantly improve the performance. Experimental results verify that the proposed method can significantly outperform existing state-of-the-art models regarding the edge prediction task on multiple homogeneous and heterogeneous graph datasets.