图像着色是计算机视觉中的一个众所周知的问题。但是，由于任务的性质不足，图像着色本质上是具有挑战性的。尽管研究人员已经进行了几次尝试制作着色管道自动化，但由于缺乏调理，这些过程通常会产生不切实际的结果。在这项工作中，我们试图将文本描述与要着色的灰度图像一起集成为辅助条件，以提高着色过程的忠诚度。据我们所知，这是将文本条件纳入着色管道中的首次尝试之一。为此，我们提出了一个新颖的深网，该网络采用了两个输入（灰度图像和相应的编码文本描述），并试图预测相关的颜色范围。由于各自的文本描述包含场景中存在的对象的颜色信息，因此文本编码有助于提高预测颜色的整体质量。我们已经使用不同的指标评估了我们提出的模型，并发现它在定性和定量上都优于最先进的着色算法。

在计算机视觉中，人类的姿势合成和转移与以前看不见的姿势的概率图像产生相关的概率图像产生。尽管研究人员最近提出了几种实现此任务的方法，但这些技术中的大多数直接从特定数据集中的所需目标图像中得出了姿势，这使得基础过程挑战在现实世界情景中应用于目标图像的生成是实际目标。在本文中，我们首先介绍当前姿势转移算法的缺点，然后提出一种新型的基于文本的姿势转移技术来解决这些问题。我们将问题分为三个独立的阶段：（a）文本构成表示，（b）姿势改进，（c）姿势渲染。据我们所知，这是开发基于文本的姿势转移框架的首次尝试之一，我们还通过为DeepFashion数据集的图像添加描述性姿势注释，从而引入了新的数据集DF-PASS。所提出的方法在我们的实验中产生了具有显着定性和定量得分的有希望的结果。

保存隐私的神经网络（NN）推理解决方案最近在几种提供不同的延迟带宽权衡的解决方案方面获得了重大吸引力。其中，许多人依靠同态加密（HE），这是一种对加密数据进行计算的方法。但是，与他们的明文对应物相比，他的操作即使是最先进的计划仍然很慢。修剪NN模型的参数是改善推理潜伏期的众所周知的方法。但是，在明文上下文中有用的修剪方法可能对HE案的改善几乎可以忽略不计，这在最近的工作中也证明了这一点。在这项工作中，我们提出了一套新颖的修剪方法，以减少潜伏期和记忆要求，从而将明文修剪方法的有效性带到HE中。至关重要的是，我们的建议采用两种关键技术，即。堆积模型权重的置换和扩展，使修剪能够明显更多的密封性下文并分别恢复大部分精度损失。我们证明了我们的方法在完全连接的层上的优势，其中使用最近提出的称为瓷砖张量的包装技术填充了权重，该技术允许在非相互作用模式下执行Deep NN推断。我们在各种自动编码器架构上评估了我们的方法，并证明，对于MNIST上的小均值重建损失为1.5*10^{ - 5}，我们将HE-SEAMABLE推断的内存要求和延迟减少了60％。

The devastation caused by the coronavirus pandemic makes it imperative to design automated techniques for a fast and accurate detection. We propose a novel non-invasive tool, using deep learning and imaging, for delineating COVID-19 infection in lungs. The Ensembling Attention-based Multi-scaled Convolution network (EAMC), employing Leave-One-Patient-Out (LOPO) training, exhibits high sensitivity and precision in outlining infected regions along with assessment of severity. The Attention module combines contextual with local information, at multiple scales, for accurate segmentation. Ensemble learning integrates heterogeneity of decision through different base classifiers. The superiority of EAMC, even with severe class imbalance, is established through comparison with existing state-of-the-art learning models over four publicly-available COVID-19 datasets. The results are suggestive of the relevance of deep learning in providing assistive intelligence to medical practitioners, when they are overburdened with patients as in pandemics. Its clinical significance lies in its unprecedented scope in providing low-cost decision-making for patients lacking specialized healthcare at remote locations.

Speech systems are sensitive to accent variations. This is especially challenging in the Indian context, with an abundance of languages but a dearth of linguistic studies characterising pronunciation variations. The growing number of L2 English speakers in India reinforces the need to study accents and L1-L2 interactions. We investigate the accents of Indian English (IE) speakers and report in detail our observations, both specific and common to all regions. In particular, we observe the phonemic variations and phonotactics occurring in the speakers' native languages and apply this to their English pronunciations. We demonstrate the influence of 18 Indian languages on IE by comparing the native language pronunciations with IE pronunciations obtained jointly from existing literature studies and phonetically annotated speech of 80 speakers. Consequently, we are able to validate the intuitions of Indian language influences on IE pronunciations by justifying pronunciation rules from the perspective of Indian language phonology. We obtain a comprehensive description in terms of universal and region-specific characteristics of IE, which facilitates accent conversion and adaptation of existing ASR and TTS systems to different Indian accents.

Due to the lack of human resources for mental health support, there is an increasing demand for employing conversational agents for support. Recent work has demonstrated the effectiveness of dialogue models in providing emotional support. As previous studies have demonstrated that seekers' persona is an important factor for effective support, we investigate whether there are benefits to modeling such information in dialogue models for support. In this paper, our empirical analysis verifies that persona has an important impact on emotional support. Therefore, we propose a framework for dynamically inferring and modeling seekers' persona. We first train a model for inferring the seeker's persona from the conversation history. Accordingly, we propose PAL, a model that leverages persona information and, in conjunction with our strategy-based controllable generation method, provides personalized emotional support. Automatic and manual evaluations demonstrate that our proposed model, PAL, achieves state-of-the-art results, outperforming the baselines on the studied benchmark. Our code and data are publicly available at https://github.com/chengjl19/PAL.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Fast timescale state estimation for a large power system can be challenging if the sensors producing the measurements are few in number. This is particularly true for doing time-synchronized state estimation for a transmission system that has minimal phasor measurement unit (PMU) coverage. This paper proposes a Deep Neural network-based State Estimator (DeNSE) to overcome this extreme unobservability problem. For systems in which the existing PMU infrastructure is not able to bring the estimation errors within acceptable limits using the DeNSE, a data-driven incremental PMU placement methodology is also introduced. The practical utility of the proposed approach is demonstrated by considering topology changes, non-Gaussian measurement noise, bad data detection and correction, and large system application.

Different types of mental rotation tests have been used extensively in psychology to understand human visual reasoning and perception. Understanding what an object or visual scene would look like from another viewpoint is a challenging problem that is made even harder if it must be performed from a single image. We explore a controlled setting whereby questions are posed about the properties of a scene if that scene was observed from another viewpoint. To do this we have created a new version of the CLEVR dataset that we call CLEVR Mental Rotation Tests (CLEVR-MRT). Using CLEVR-MRT we examine standard methods, show how they fall short, then explore novel neural architectures that involve inferring volumetric representations of a scene. These volumes can be manipulated via camera-conditioned transformations to answer the question. We examine the efficacy of different model variants through rigorous ablations and demonstrate the efficacy of volumetric representations.

State-of-the-art object detectors are fast and accurate, but they require a large amount of well annotated training data to obtain good performance. However, obtaining a large amount of training annotations specific to a particular task, i.e., fine-grained annotations, is costly in practice. In contrast, obtaining common-sense relationships from text, e.g., "a table-lamp is a lamp that sits on top of a table", is much easier. Additionally, common-sense relationships like "on-top-of" are easy to annotate in a task-agnostic fashion. In this paper, we propose a probabilistic model that uses such relational knowledge to transform an off-the-shelf detector of coarse object categories (e.g., "table", "lamp") into a detector of fine-grained categories (e.g., "table-lamp"). We demonstrate that our method, RelDetect, achieves performance competitive to finetuning based state-of-the-art object detector baselines when an extremely low amount of fine-grained annotations is available ($0.2\%$ of entire dataset). We also demonstrate that RelDetect is able to utilize the inherent transferability of relationship information to obtain a better performance ($+5$ mAP points) than the above baselines on an unseen dataset (zero-shot transfer). In summary, we demonstrate the power of using relationships for object detection on datasets where fine-grained object categories can be linked to coarse-grained categories via suitable relationships.