Recent advances in pixel-level tasks (e.g., segmentation) illustrate the benefit of long-range interactions between aggregated region-based representations that can enhance local features. However, such pixel-to-region associations and the resulting representation, which often take the form of attention, cannot model the underlying semantic structure of the scene (e.g., individual objects and, by extension, their interactions). In this work, we take a step toward addressing this limitation. Specifically, we propose an architecture where we learn to project image features into latent region representations and perform global reasoning across them, using a transformer, to produce contextualized and scene-consistent representations that are then fused with original pixel-level features. Our design enables the latent regions to represent semantically meaningful concepts, by ensuring that activated regions are spatially disjoint and unions of such regions correspond to connected object segments. The resulting semantic global reasoning (SGR) is end-to-end trainable and can be combined with any semantic segmentation framework and backbone. Combining SGR with DeepLabV3 results in a semantic segmentation performance that is competitive to the state-of-the-art, while resulting in more semantically interpretable and diverse region representations, which we show can effectively transfer to detection and instance segmentation. Further, we propose a new metric that allows us to measure the semantics of representations at both the object class and instance level.

神经辐射场（NERFS）增加了新型视图合成和场景重建的重建细节，其应用程序从大型静态场景到动态人类运动不等。但是，此类神经领域的分辨率和无模型性质的增加是以高训练时间和过度记忆要求为代价的。最近的进步通过使用互补的数据结构改善了推理时间，但这些方法不适合动态场景，并且通常会增加记忆消耗。减少培训时所需的资源几乎没有做到。我们提出了一种方法，通过部分共享相邻样本点的评估来利用NERF基于样本的计算的冗余。我们的UNERF体系结构的灵感来自UNET，该架构在网络中间减少空间分辨率，并在相邻样本之间共享信息。尽管这种变化违反了NERF方法中的严格和有意识的依赖性外观和无关的密度估计的分离，但我们表明它改善了新型观点的综合。我们还引入了一种替代性亚采样策略，该策略共享计算，同时最大程度地减少视图不变性的侵犯。 UNERF是原始NERF网络的插件模块。我们的主要贡献包括减少记忆足迹，提高准确性以及在训练和推理期间摊销的处理时间减少。在当地的假设较弱的情况下，我们在各种神经辐射场任务上实现了改进的资源利用。我们演示了对静态场景的新观点综合以及动态人类形状和运动的应用。

从单个图像的人类姿势估计是一个充满挑战的问题，通常通过监督学习解决。不幸的是，由于3D注释需要专用的运动捕获系统，因此许多人类活动尚不存在标记的培训数据。因此，我们提出了一种无监督的方法，该方法学会从单个图像预测3D人类姿势，同时只有2D姿势数据培训，这可能是人群的并且已经广泛可用。为此，我们估计最有可能过于随机投影的3D姿势，其中使用2D姿势的归一化流程估计的可能性。虽然以前的工作需要在训练数据集中的相机旋转上需要强大的前锋，但我们了解了相机角度的分布，显着提高了性能。我们的贡献的另一部分是通过首先将2D突出到线性子空间来稳定高维3D姿势数据上的标准化流动的训练。在许多指标中，我们优于基准数据集Humanets3.6m和MPI-INF-3DHP的最先进的无人监督的人类姿势估算方法。

日益复杂的机器学习模型的不断增长的计算需求通常需要使用强大的基于云的基础架构进行培训。已知二元神经网络由于其极端的计算和内存节省了更高精确的替代方案，因此有望进行现场推断。但是，他们现有的训练方法需要同时存储所有层的高精度激活，这通常使在内存受限的设备上学习不可行。在本文中，我们证明了二进制神经网络训练所需的向后传播操作对量化非常强大，从而使现代模型的现场学习成为实用命题。我们介绍了一种低成本的二元神经网络训练策略，该策略表现出相当大的记忆范围减少，同时几乎没有准确的损失与Courbariaux＆Bengio的标准方法。这些减少主要是通过仅以二进制格式保留激活来实现的。在后一种算法上，我们的置换替换量看到记忆需求减少3--5 $ \ times $，同时在可比时间内达到相似的测试准确性，这些型号跨越了一系列经过培训的小型模型，用于对流行数据集进行分类。我们还展示了对二进制RESNET-18的从划痕成像网训练，并实现了3.78 $ \ times $减少内存。我们的工作是开源的，包括覆盆子Pi靶向原型，我们用来验证建模的内存降低并捕获相关的能量滴。这样的节省将避免不必要的云下载，减少延迟，提高能源效率和保护最终用户的隐私。

Following the success of deep convolutional networks, state-of-the-art methods for 3d human pose estimation have focused on deep end-to-end systems that predict 3d joint locations given raw image pixels. Despite their excellent performance, it is often not easy to understand whether their remaining error stems from a limited 2d pose (visual) understanding, or from a failure to map 2d poses into 3dimensional positions.With the goal of understanding these sources of error, we set out to build a system that given 2d joint locations predicts 3d positions. Much to our surprise, we have found that, with current technology, "lifting" ground truth 2d joint locations to 3d space is a task that can be solved with a remarkably low error rate: a relatively simple deep feedforward network outperforms the best reported result by about 30% on Human3.6M, the largest publicly available 3d pose estimation benchmark. Furthermore, training our system on the output of an off-the-shelf state-of-the-art 2d detector (i.e., using images as input) yields state of the art results -this includes an array of systems that have been trained end-to-end specifically for this task. Our results indicate that a large portion of the error of modern deep 3d pose estimation systems stems from their visual analysis, and suggests directions to further advance the state of the art in 3d human pose estimation.

Knowledge distillation (KD) has gained a lot of attention in the field of model compression for edge devices thanks to its effectiveness in compressing large powerful networks into smaller lower-capacity models. Online distillation, in which both the teacher and the student are learning collaboratively, has also gained much interest due to its ability to improve on the performance of the networks involved. The Kullback-Leibler (KL) divergence ensures the proper knowledge transfer between the teacher and student. However, most online KD techniques present some bottlenecks under the network capacity gap. By cooperatively and simultaneously training, the models the KL distance becomes incapable of properly minimizing the teacher's and student's distributions. Alongside accuracy, critical edge device applications are in need of well-calibrated compact networks. Confidence calibration provides a sensible way of getting trustworthy predictions. We propose BD-KD: Balancing of Divergences for online Knowledge Distillation. We show that adaptively balancing between the reverse and forward divergences shifts the focus of the training strategy to the compact student network without limiting the teacher network's learning process. We demonstrate that, by performing this balancing design at the level of the student distillation loss, we improve upon both performance accuracy and calibration of the compact student network. We conducted extensive experiments using a variety of network architectures and show improvements on multiple datasets including CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet. We illustrate the effectiveness of our approach through comprehensive comparisons and ablations with current state-of-the-art online and offline KD techniques.

Fine-tuning a Pre-trained Language Model (PLM) on a specific downstream task has been a well-known paradigm in Natural Language Processing. However, with the ever-growing size of PLMs, training the entire model on several downstream tasks becomes very expensive and resource-hungry. Recently, different Parameter Efficient Tuning (PET) techniques are proposed to improve the efficiency of fine-tuning PLMs. One popular category of PET methods is the low-rank adaptation methods which insert learnable truncated SVD modules into the original model either sequentially or in parallel. However, low-rank decomposition suffers from limited representation power. In this work, we address this problem using the Kronecker product instead of the low-rank representation. We introduce KronA, a Kronecker product-based adapter module for efficient fine-tuning of Transformer-based PLMs. We apply the proposed methods for fine-tuning T5 on the GLUE benchmark to show that incorporating the Kronecker-based modules can outperform state-of-the-art PET methods.

Developments in autonomous vehicles (AVs) are rapidly advancing and will in the next 20 years become a central part to our society. However, especially in the early stages of deployment, there is expected to be incidents involving AVs. In the event of AV incidents, decisions will need to be made that require ethical decisions, e.g., deciding between colliding into a group of pedestrians or a rigid barrier. For an AV to undertake such ethical decision making and path planning, simulation models of the situation will be required that are used in real-time on-board the AV. These models will enable path planning and ethical decision making to be undertaken based on predetermined collision injury severity levels. In this research, models are developed for the path planning and ethical decision making that predetermine knowledge regarding the possible collision injury severities, i.e., peak deformation of the AV colliding into the rigid barrier or the impact velocity of the AV colliding into a pedestrian. Based on such knowledge and using fuzzy logic, a novel nonlinear weighted utility cost function for the collision injury severity levels is developed. This allows the model-based predicted collision outcomes arising from AV peak deformation and AV-pedestrian impact velocity to be examined separately via weighted utility cost functions with a common structure. The general form of the weighted utility cost function exploits a fuzzy sets approach, thus allowing common utility costs from the two separate utility cost functions to be meaningfully compared. A decision-making algorithm, which makes use of a utilitarian ethical approach, ensures that the AV will always steer onto the path which represents the lowest injury severity level, hence utility cost to society.

Electronic Health Records (EHRs) hold detailed longitudinal information about each patient's health status and general clinical history, a large portion of which is stored within the unstructured text. Temporal modelling of this medical history, which considers the sequence of events, can be used to forecast and simulate future events, estimate risk, suggest alternative diagnoses or forecast complications. While most prediction approaches use mainly structured data or a subset of single-domain forecasts and outcomes, we processed the entire free-text portion of EHRs for longitudinal modelling. We present Foresight, a novel GPT3-based pipeline that uses NER+L tools (i.e. MedCAT) to convert document text into structured, coded concepts, followed by providing probabilistic forecasts for future medical events such as disorders, medications, symptoms and interventions. Since large portions of EHR data are in text form, such an approach benefits from a granular and detailed view of a patient while introducing modest additional noise. On tests in two large UK hospitals (King's College Hospital, South London and Maudsley) and the US MIMIC-III dataset precision@10 of 0.80, 0.81 and 0.91 was achieved for forecasting the next biomedical concept. Foresight was also validated on 34 synthetic patient timelines by 5 clinicians and achieved relevancy of 97% for the top forecasted candidate disorder. Foresight can be easily trained and deployed locally as it only requires free-text data (as a minimum). As a generative model, it can simulate follow-on disorders, medications and interventions for as many steps as required. Foresight is a general-purpose model for biomedical concept modelling that can be used for real-world risk estimation, virtual trials and clinical research to study the progression of diseases, simulate interventions and counterfactuals, and for educational purposes.

As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adapt in dynamically-changing environments, becoming Lifelong Learning Machines. Continual/lifelong learning (LL) involves minimizing catastrophic forgetting of old tasks while maximizing a model's capability to learn new tasks. This paper addresses the challenging lifelong reinforcement learning (L2RL) setting. Pushing the state-of-the-art forward in L2RL and making L2RL useful for practical applications requires more than developing individual L2RL algorithms; it requires making progress at the systems-level, especially research into the non-trivial problem of how to integrate multiple L2RL algorithms into a common framework. In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system. As an instantiation of L2RLCF, we develop a standard API allowing easy integration of novel lifelong learning components. We describe a case study that demonstrates how multiple independently-developed LL components can be integrated into a single realized system. We also introduce an evaluation environment in order to measure the effect of combining various system components. Our evaluation environment employs different LL scenarios (sequences of tasks) consisting of Starcraft-2 minigames and allows for the fair, comprehensive, and quantitative comparison of different combinations of components within a challenging common evaluation environment.