未配对的图像翻译算法可用于SIM2REAL任务，但许多人无法生成时间一致的结果。我们提出了一种新的方法，该方法将可区分的渲染与图像翻译结合在一起，以使用表面一致性损失和\ emph {神经神经纹理}来实现无限时间尺度上的时间一致性。我们称此算法Triton（纹理恢复图像翻译网络）：一种无监督的，端到端的，无状态的SIM2REAL算法，通过生成现实的可学习神经纹理来利用输入场景的基础3D几何。通过在场景中的对象上安顿特定的纹理，我们确保框架之间的一致性。与以前的算法不同，Triton不仅限于相机的运动 - 它也可以处理对象的运动，使其可用于诸如机器人操纵之类的下游任务。

人类在理解视觉皮层引起的观点变化方面非常灵活，从而支持3D结构的感知。相反，大多数从2D图像池学习视觉表示的计算机视觉模型通常无法概括新颖的相机观点。最近，视觉体系结构已转向无卷积的架构，视觉变压器，该构造在从图像贴片中得出的令牌上运行。但是，这些变压器和2D卷积网络都没有执行明确的操作来学习视图 - 不合稳定表示以进行视觉理解。为此，我们提出了一个3D令牌表示层（3DTRL），该层估计了视觉令牌的3D位置信息，并利用它来学习视图点 - 不可能的表示。 3DTRL的关键元素包括伪深度估计器和学习的相机矩阵，以对令牌施加几何变换。这些使3DTRL能够从2D贴片中恢复令牌的3D位置信息。实际上，3DTRL很容易插入变压器。我们的实验证明了3DTRL在许多视觉任务中的有效性，包括图像分类，多视频视频对准和动作识别。带有3DTRL的模型在所有任务中都超过了骨干变压器，并以最小的添加计算。我们的项目页面位于https://www3.cs.stonybrook.edu/~jishang/3dtrl/3dtrl.html

我们研究自我监督学习（SSL）是否可以从像素中改善在线增强学习（RL）。我们扩展了对比度增强学习框架（例如卷曲），该框架共同优化了SSL和RL损失，并进行了大量的实验，并具有各种自我监督的损失。我们的观察结果表明，现有的RL的SSL框架未能在使用相同数量的数据和增强时利用图像增强来实现对基准的有意义的改进。我们进一步执行进化搜索，以找到RL的多个自我监督损失的最佳组合，但是发现即使是这种损失组合也无法有意义地超越仅利用精心设计的图像增强的方法。通常，在现有框架下使用自制损失降低了RL性能。我们在多个不同环境中评估了该方法，包括现实世界的机器人环境，并确认没有任何单一的自我监督损失或图像增强方法可以主导所有环境，并且当前的SSL和RL联合优化框架是有限的。最后，我们从经验上研究了SSL + RL的预训练框架以及使用不同方法学到的表示的特性。

强化学习（RL）可以视为序列建模任务：给定一系列过去的状态奖励经验，代理人预测了下一步动作的序列。在这项工作中，我们提出了用于视觉RL的国家行动 - 奖励变压器（星形形式），该变压器明确对短期状态行动奖励表示（Star-epresentations）进行建模，从本质上引入了马尔可夫式的感应偏见，以改善长期的长期偏见造型。我们的方法首先通过在短暂的时间窗口内的自我管理图像状态贴片，动作和奖励令牌提取星星代表。然后将它们与纯图像状态表示结合 - 提取为卷积特征，以在整个序列上执行自我注意力。我们的实验表明，在离线RL和模仿学习设置中，StarFormer在基于图像的Atari和DeepMind Control Suite基准上的最先进的变压器方法优于最先进的变压器方法。 StarFormer也更符合更长的输入序列。我们的代码可在https://github.com/elicassion/starformer上找到。

Existing federated classification algorithms typically assume the local annotations at every client cover the same set of classes. In this paper, we aim to lift such an assumption and focus on a more general yet practical non-IID setting where every client can work on non-identical and even disjoint sets of classes (i.e., client-exclusive classes), and the clients have a common goal which is to build a global classification model to identify the union of these classes. Such heterogeneity in client class sets poses a new challenge: how to ensure different clients are operating in the same latent space so as to avoid the drift after aggregation? We observe that the classes can be described in natural languages (i.e., class names) and these names are typically safe to share with all parties. Thus, we formulate the classification problem as a matching process between data representations and class representations and break the classification model into a data encoder and a label encoder. We leverage the natural-language class names as the common ground to anchor the class representations in the label encoder. In each iteration, the label encoder updates the class representations and regulates the data representations through matching. We further use the updated class representations at each round to annotate data samples for locally-unaware classes according to similarity and distill knowledge to local models. Extensive experiments on four real-world datasets show that the proposed method can outperform various classical and state-of-the-art federated learning methods designed for learning with non-IID data.

Existing measures and representations for trajectories have two longstanding fundamental shortcomings, i.e., they are computationally expensive and they can not guarantee the `uniqueness' property of a distance function: dist(X,Y) = 0 if and only if X=Y, where $X$ and $Y$ are two trajectories. This paper proposes a simple yet powerful way to represent trajectories and measure the similarity between two trajectories using a distributional kernel to address these shortcomings. It is a principled approach based on kernel mean embedding which has a strong theoretical underpinning. It has three distinctive features in comparison with existing approaches. (1) A distributional kernel is used for the very first time for trajectory representation and similarity measurement. (2) It does not rely on point-to-point distances which are used in most existing distances for trajectories. (3) It requires no learning, unlike existing learning and deep learning approaches. We show the generality of this new approach in three applications: (a) trajectory anomaly detection, (b) anomalous sub-trajectory detection, and (c) trajectory pattern mining. We identify that the distributional kernel has (i) a unique data-dependent property and the above uniqueness property which are the key factors that lead to its superior task-specific performance; and (ii) runtime orders of magnitude faster than existing distance measures.

Natural Language Processing (NLP) has been revolutionized by the use of Pre-trained Language Models (PLMs) such as BERT. Despite setting new records in nearly every NLP task, PLMs still face a number of challenges including poor interpretability, weak reasoning capability, and the need for a lot of expensive annotated data when applied to downstream tasks. By integrating external knowledge into PLMs, \textit{\underline{K}nowledge-\underline{E}nhanced \underline{P}re-trained \underline{L}anguage \underline{M}odels} (KEPLMs) have the potential to overcome the above-mentioned limitations. In this paper, we examine KEPLMs systematically through a series of studies. Specifically, we outline the common types and different formats of knowledge to be integrated into KEPLMs, detail the existing methods for building and evaluating KEPLMS, present the applications of KEPLMs in downstream tasks, and discuss the future research directions. Researchers will benefit from this survey by gaining a quick and comprehensive overview of the latest developments in this field.

Autonomous robotic surgery has advanced significantly based on analysis of visual and temporal cues in surgical workflow, but relational cues from domain knowledge remain under investigation. Complex relations in surgical annotations can be divided into intra- and inter-relations, both valuable to autonomous systems to comprehend surgical workflows. Intra- and inter-relations describe the relevance of various categories within a particular annotation type and the relevance of different annotation types, respectively. This paper aims to systematically investigate the importance of relational cues in surgery. First, we contribute the RLLS12M dataset, a large-scale collection of robotic left lateral sectionectomy (RLLS), by curating 50 videos of 50 patients operated by 5 surgeons and annotating a hierarchical workflow, which consists of 3 inter- and 6 intra-relations, 6 steps, 15 tasks, and 38 activities represented as the triplet of 11 instruments, 8 actions, and 16 objects, totaling 2,113,510 video frames and 12,681,060 annotation entities. Correspondingly, we propose a multi-relation purification hybrid network (MURPHY), which aptly incorporates novel relation modules to augment the feature representation by purifying relational features using the intra- and inter-relations embodied in annotations. The intra-relation module leverages a R-GCN to implant visual features in different graph relations, which are aggregated using a targeted relation purification with affinity information measuring label consistency and feature similarity. The inter-relation module is motivated by attention mechanisms to regularize the influence of relational features based on the hierarchy of annotation types from the domain knowledge. Extensive experimental results on the curated RLLS dataset confirm the effectiveness of our approach, demonstrating that relations matter in surgical workflow analysis.

Deep learning-based methods have achieved significant performance for image defogging. However, existing methods are mainly developed for land scenes and perform poorly when dealing with overwater foggy images, since overwater scenes typically contain large expanses of sky and water. In this work, we propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes. To promote the recovery of the objects on water in the image, two loss functions are exploited for the network where a prior map is designed to invert the dark channel and the min-max normalization is used to suppress the sky and emphasize objects. However, due to the unpaired training set, the network may learn an under-constrained domain mapping from foggy to fog-free image, leading to artifacts and loss of details. Thus, we propose an intuitive Upscaling Inception Module (UIM) and a Long-range Residual Coarse-to-fine framework (LRC) to mitigate this issue. Extensive experiments on qualitative and quantitative comparisons demonstrate that the proposed method outperforms the state-of-the-art supervised, semi-supervised, and unsupervised defogging approaches.

Code generation models have achieved impressive performance. However, they tend to be brittle as slight edits to a prompt could lead to very different generations; these robustness properties, critical for user experience when deployed in real-life applications, are not well understood. Most existing works on robustness in text or code tasks have focused on classification, while robustness in generation tasks is an uncharted area and to date there is no comprehensive benchmark for robustness in code generation. In this paper, we propose ReCode, a comprehensive robustness evaluation benchmark for code generation models. We customize over 30 transformations specifically for code on docstrings, function and variable names, code syntax, and code format. They are carefully designed to be natural in real-life coding practice, preserve the original semantic meaning, and thus provide multifaceted assessments of a model's robustness performance. With human annotators, we verified that over 90% of the perturbed prompts do not alter the semantic meaning of the original prompt. In addition, we define robustness metrics for code generation models considering the worst-case behavior under each type of perturbation, taking advantage of the fact that executing the generated code can serve as objective evaluation. We demonstrate ReCode on SOTA models using HumanEval, MBPP, as well as function completion tasks derived from them. Interesting observations include: better robustness for CodeGen over InCoder and GPT-J; models are most sensitive to syntax perturbations; more challenging robustness evaluation on MBPP over HumanEval.