最近，大规模文本驱动的合成模型由于其出色的产生高度多样化的图像而引起了很多关注，这些图像遵循给定的文本提示。这种基于文本的综合方法特别有吸引力，这些方法对人类用来口头描述其意图。因此，将文本驱动的图像合成扩展到文本驱动的图像编辑是很自然的。编辑对于这些生成模型来说是具有挑战性的，因为编辑技术的先天属性是保留大多数原始图像，而在基于文本的模型中，即使对文本提示的小修改也通常会导致完全不同的结果。最先进的方法可以通过要求用户提供空间掩码来本地化编辑，从而忽略蒙版区域内的原始结构和内容，从而减轻这种方法。在本文中，我们追求一个直观的及时提示编辑框架，其中编辑仅由文本控制。为此，我们深入分析了一个文本条件模型，并观察到跨注意层是控制图像的空间布局与提示中每个单词之间关系的关键。通过此观察，我们提出了几种应用程序，它们仅通过编辑文本提示来监视图像综合。这包括通过替换单词，通过添加规范来替换单词编辑的本地化编辑，甚至精心控制单词在图像中反映的程度。我们介绍了各种图像和提示的结果，证明了对编辑提示的高质量综合和忠诚度。

真实图像进入样式中的潜在空间是一个研究的问题。然而，由于重建和可编辑性之间的固有权衡，将现有的现实情景方法应用于现实世界的情况仍然是一个开放的挑战：可以准确代表真实图像的潜在空间区域通常遭受降级的语义控制。最近的工作提出通过微调发电机将目标图像添加到潜在空间的良好编辑区域来减轻此权衡。在有希望的同时，这种微调方案对于普遍使用而言是不切实际的，因为它需要每个新图像需要冗长的训练阶段。在这项工作中，我们将这种方法介绍到基于编码器的反演的领域。我们提出了一个HyperSTYLE，一个高度作品，用于学习调制Stylegan权重，以忠实地在潜在空间的可编辑区域中表达给定的图像。一个天真的调制方法需要培训超过30亿参数的高度工作。通过仔细的网络设计，我们将其降低到与现有的编码器一致。 Hyperstyle产生与具有编码器的近实时推理能力的优化技术相当的重建。最后，我们展示了超出了超出了反转任务的若干应用的效力，包括编辑域名域名的域外图像。

图像标题是视觉语言理解的基本任务，其中模型将文本信息标题预测到给定输入图像。在本文中，我们提出了一种解决此任务的简单方法。我们使用剪辑编码作为标题的前缀，通过采用简单的映射网络，然后微调语言模型以生成图像标题。最近提出的剪辑模型包含丰富的语义特征，这些功能培训了文本背景，使其最适合视觉语言感知。我们的关键思想与预先接受训练的语言模型（GPT2）一起，我们获得了广泛了解视觉和文本数据。因此，我们的方法只需要相当快速的培训来产生称职的标题模型。如果没有额外的注释或预训练，它有效地为大规模和多样化的数据集生成有意义的标题。令人惊讶的是，即使仅在训练映射网络时，我们的方法也很好地运行良好，而剪辑和语言模型仍然冻结，则允许较轻的培训参数较轻的架构。通过定量评估，我们展示了我们的模型在充满挑战的概念标题和Nocaps数据集上实现了最先进的方法的可比结果，而它更简单，更快，更轻。我们的代码在https://github.com/rmokady/clip_prefix_caption中提供。

Neural Representations have recently been shown to effectively reconstruct a wide range of signals from 3D meshes and shapes to images and videos. We show that, when adapted correctly, neural representations can be used to directly represent the weights of a pre-trained convolutional neural network, resulting in a Neural Representation for Neural Networks (NeRN). Inspired by coordinate inputs of previous neural representation methods, we assign a coordinate to each convolutional kernel in our network based on its position in the architecture, and optimize a predictor network to map coordinates to their corresponding weights. Similarly to the spatial smoothness of visual scenes, we show that incorporating a smoothness constraint over the original network's weights aids NeRN towards a better reconstruction. In addition, since slight perturbations in pre-trained model weights can result in a considerable accuracy loss, we employ techniques from the field of knowledge distillation to stabilize the learning process. We demonstrate the effectiveness of NeRN in reconstructing widely used architectures on CIFAR-10, CIFAR-100, and ImageNet. Finally, we present two applications using NeRN, demonstrating the capabilities of the learned representations.

A core process in human cognition is analogical mapping: the ability to identify a similar relational structure between different situations. We introduce a novel task, Visual Analogies of Situation Recognition, adapting the classical word-analogy task into the visual domain. Given a triplet of images, the task is to select an image candidate B' that completes the analogy (A to A' is like B to what?). Unlike previous work on visual analogy that focused on simple image transformations, we tackle complex analogies requiring understanding of scenes. We leverage situation recognition annotations and the CLIP model to generate a large set of 500k candidate analogies. Crowdsourced annotations for a sample of the data indicate that humans agree with the dataset label ~80% of the time (chance level 25%). Furthermore, we use human annotations to create a gold-standard dataset of 3,820 validated analogies. Our experiments demonstrate that state-of-the-art models do well when distractors are chosen randomly (~86%), but struggle with carefully chosen distractors (~53%, compared to 90% human accuracy). We hope our dataset will encourage the development of new analogy-making models. Website: https://vasr-dataset.github.io/

Micron-scale robots (ubots) have recently shown great promise for emerging medical applications, and accurate control of ubots is a critical next step to deploying them in real systems. In this work, we develop the idea of a nonlinear mismatch controller to compensate for the mismatch between the disturbed unicycle model of a rolling ubot and trajectory data collected during an experiment. We exploit the differential flatness property of the rolling ubot model to generate a mapping from the desired state trajectory to nominal control actions. Due to model mismatch and parameter estimation error, the nominal control actions will not exactly reproduce the desired state trajectory. We employ a Gaussian Process (GP) to learn the model mismatch as a function of the desired control actions, and correct the nominal control actions using a least-squares optimization. We demonstrate the performance of our online learning algorithm in simulation, where we show that the model mismatch makes some desired states unreachable. Finally, we validate our approach in an experiment and show that the error metrics are reduced by up to 40%.

A master face is a face image that passes face-based identity authentication for a high percentage of the population. These faces can be used to impersonate, with a high probability of success, any user, without having access to any user information. We optimize these faces for 2D and 3D face verification models, by using an evolutionary algorithm in the latent embedding space of the StyleGAN face generator. For 2D face verification, multiple evolutionary strategies are compared, and we propose a novel approach that employs a neural network to direct the search toward promising samples, without adding fitness evaluations. The results we present demonstrate that it is possible to obtain a considerable coverage of the identities in the LFW or RFW datasets with less than 10 master faces, for six leading deep face recognition systems. In 3D, we generate faces using the 2D StyleGAN2 generator and predict a 3D structure using a deep 3D face reconstruction network. When employing two different 3D face recognition systems, we are able to obtain a coverage of 40%-50%. Additionally, we present the generation of paired 2D RGB and 3D master faces, which simultaneously match 2D and 3D models with high impersonation rates.

The use of needles to access sites within organs is fundamental to many interventional medical procedures both for diagnosis and treatment. Safe and accurate navigation of a needle through living tissue to an intra-tissue target is currently often challenging or infeasible due to the presence of anatomical obstacles in the tissue, high levels of uncertainty, and natural tissue motion (e.g., due to breathing). Medical robots capable of automating needle-based procedures in vivo have the potential to overcome these challenges and enable an enhanced level of patient care and safety. In this paper, we show the first medical robot that autonomously navigates a needle inside living tissue around anatomical obstacles to an intra-tissue target. Our system leverages an aiming device and a laser-patterned highly flexible steerable needle, a type of needle capable of maneuvering along curvilinear trajectories to avoid obstacles. The autonomous robot accounts for anatomical obstacles and uncertainty in living tissue/needle interaction with replanning and control and accounts for respiratory motion by defining safe insertion time windows during the breathing cycle. We apply the system to lung biopsy, which is critical in the diagnosis of lung cancer, the leading cause of cancer-related death in the United States. We demonstrate successful performance of our system in multiple in vivo porcine studies and also demonstrate that our approach leveraging autonomous needle steering outperforms a standard manual clinical technique for lung nodule access.

The field of emergent communication aims to understand the characteristics of communication as it emerges from artificial agents solving tasks that require information exchange. Communication with discrete messages is considered a desired characteristic, for both scientific and applied reasons. However, training a multi-agent system with discrete communication is not straightforward, requiring either reinforcement learning algorithms or relaxing the discreteness requirement via a continuous approximation such as the Gumbel-softmax. Both these solutions result in poor performance compared to fully continuous communication. In this work, we propose an alternative approach to achieve discrete communication -- quantization of communicated messages. Using message quantization allows us to train the model end-to-end, achieving superior performance in multiple setups. Moreover, quantization is a natural framework that runs the gamut from continuous to discrete communication. Thus, it sets the ground for a broader view of multi-agent communication in the deep learning era.

在视频分析中，背景模型具有许多应用，例如背景/前景分离，变更检测，异常检测，跟踪等。但是，尽管在静态相机捕获的视频中学习这种模型是一项公认的任务，但在移动相机背景模型（MCBM）的情况下，由于算法和可伸缩性挑战，成功率更加重要。由于相机运动而产生。因此，现有的MCBM在其范围和受支持的摄像头类型的限制中受到限制。这些障碍还阻碍了基于深度学习（DL）的端到端解决方案的这项无监督的任务。此外，现有的MCBM通常会在典型的大型全景图像或以在线方式的域名上建模背景。不幸的是，前者造成了几个问题，包括可扩展性差，而后者则阻止了对摄像机重新审视场景先前看到部分的案例的识别和利用。本文提出了一种称为DEEPMCBM的新方法，该方法消除了上述所有问题并实现最新结果。具体而言，首先，我们确定与一般和DL设置的视频帧联合对齐相关的困难。接下来，我们提出了一种新的联合一致性策略，使我们可以使用具有正则化的空间变压器网，也不是任何形式的专业化（且不差异）的初始化。再加上在不破坏的稳健中央矩（从关节对齐中获得）的自动编码器，这产生了一个无端到端的无端正规化MCBM，该MCBM支持广泛的摄像机运动并优雅地缩放。我们在各种视频上展示了DEEPMCBM的实用程序，包括超出其他方法范围的视频。我们的代码可在https://github.com/bgu-cs-vil/deepmcbm上找到。