Most approaches for semantic segmentation use only information from color cameras to parse the scenes, yet recent advancements show that using depth data allows to further improve performances. In this work, we focus on transformer-based deep learning architectures, that have achieved state-of-the-art performances on the segmentation task, and we propose to employ depth information by embedding it in the positional encoding. Effectively, we extend the network to multimodal data without adding any parameters and in a natural way that makes use of the strength of transformers' self-attention modules. We also investigate the idea of performing cross-modality operations inside the attention module, swapping the key inputs between the depth and color branches. Our approach consistently improves performances on the Cityscapes benchmark.

间接飞行时间摄像机（ITOF）是低成本设备，其以交互式帧速率提供深度图像。但是，它们受到不同误差源的影响，具有多路径干扰（MPI）的聚光灯，这项技术的关键挑战。常见的数据驱动方法倾向于专注于输出深度值的直接估计，忽略场景中光的底层瞬态传播。在这项工作中，我们提出了一个非常紧凑的架构，利用了用于去除MPI的瞬态信息的直接全球性，并为瞬态信息本身重建。所提出的模型达到了最先进的MPI校正性能，也可以在综合和实际数据上进行，并且在极端的噪声中也被证明是非常竞争力的;同时，它还致力于重建来自多频I IOF数据的瞬态信息。

深度神经网络在学习新任务时遭受灾难性遗忘的主要限制。在本文中，我们专注于语义细分中的课堂持续学习，其中新类别随着时间的推移，而在未保留以前的训练数据。建议的持续学习方案塑造了潜在的空间来减少遗忘，同时提高了对新型课程的识别。我们的框架是由三种新的组件驱动，我们还毫不费力地结合现有的技术。首先，匹配的原型匹配在旧类上强制执行潜在空间一致性，约束编码器在后续步骤中为先前看到的类生成类似的潜在潜在表示。其次，特征稀疏性允许在潜在空间中腾出空间以容纳新型课程。最后，根据他们的语义，在统一的同时撕裂不同类别的语义，对形成对比的学习。对Pascal VOC2012和ADE20K数据集的广泛评估展示了我们方法的有效性，显着优于最先进的方法。

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.

Artificial neural networks can learn complex, salient data features to achieve a given task. On the opposite end of the spectrum, mathematically grounded methods such as topological data analysis allow users to design analysis pipelines fully aware of data constraints and symmetries. We introduce a class of persistence-based neural network layers. Persistence-based layers allow the users to easily inject knowledge about symmetries (equivariance) respected by the data, are equipped with learnable weights, and can be composed with state-of-the-art neural architectures.

Quantifying motion in 3D is important for studying the behavior of humans and other animals, but manual pose annotations are expensive and time-consuming to obtain. Self-supervised keypoint discovery is a promising strategy for estimating 3D poses without annotations. However, current keypoint discovery approaches commonly process single 2D views and do not operate in the 3D space. We propose a new method to perform self-supervised keypoint discovery in 3D from multi-view videos of behaving agents, without any keypoint or bounding box supervision in 2D or 3D. Our method uses an encoder-decoder architecture with a 3D volumetric heatmap, trained to reconstruct spatiotemporal differences across multiple views, in addition to joint length constraints on a learned 3D skeleton of the subject. In this way, we discover keypoints without requiring manual supervision in videos of humans and rats, demonstrating the potential of 3D keypoint discovery for studying behavior.

Artificial intelligence is set to be deployed in operating rooms to improve surgical care. This early-stage clinical evaluation shows the feasibility of concurrently attaining real-time, high-quality predictions from several deep neural networks for endoscopic video analysis deployed for assistance during three laparoscopic cholecystectomies.

AI-based code generators are an emerging solution for automatically writing programs starting from descriptions in natural language, by using deep neural networks (Neural Machine Translation, NMT). In particular, code generators have been used for ethical hacking and offensive security testing by generating proof-of-concept attacks. Unfortunately, the evaluation of code generators still faces several issues. The current practice uses automatic metrics, which compute the textual similarity of generated code with ground-truth references. However, it is not clear what metric to use, and which metric is most suitable for specific contexts. This practical experience report analyzes a large set of output similarity metrics on offensive code generators. We apply the metrics on two state-of-the-art NMT models using two datasets containing offensive assembly and Python code with their descriptions in the English language. We compare the estimates from the automatic metrics with human evaluation and provide practical insights into their strengths and limitations.

Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their exposure. In this work, we propose to capture each of these aspects by modeling the surgical scene with a disentangled latent scene graph representation, which we can then process using a graph neural network. Unlike previous approaches using graph representations, we explicitly encode in our graphs semantic information such as object locations and shapes, class probabilities and visual features. We also incorporate an auxiliary image reconstruction objective to help train the latent graph representations. We demonstrate the value of these components through comprehensive ablation studies and achieve state-of-the-art results for critical view of safety prediction across multiple experimental settings.

Searching for a path between two nodes in a graph is one of the most well-studied and fundamental problems in computer science. In numerous domains such as robotics, AI, or biology, practitioners develop search heuristics to accelerate their pathfinding algorithms. However, it is a laborious and complex process to hand-design heuristics based on the problem and the structure of a given use case. Here we present PHIL (Path Heuristic with Imitation Learning), a novel neural architecture and a training algorithm for discovering graph search and navigation heuristics from data by leveraging recent advances in imitation learning and graph representation learning. At training time, we aggregate datasets of search trajectories and ground-truth shortest path distances, which we use to train a specialized graph neural network-based heuristic function using backpropagation through steps of the pathfinding process. Our heuristic function learns graph embeddings useful for inferring node distances, runs in constant time independent of graph sizes, and can be easily incorporated in an algorithm such as A* at test time. Experiments show that PHIL reduces the number of explored nodes compared to state-of-the-art methods on benchmark datasets by 58.5\% on average, can be directly applied in diverse graphs ranging from biological networks to road networks, and allows for fast planning in time-critical robotics domains.