Single-image super-resolution (SISR) networks trained with perceptual and adversarial losses provide high-contrast outputs compared to those of networks trained with distortion-oriented losses, such as L1 or L2. However, it has been shown that using a single perceptual loss is insufficient for accurately restoring locally varying diverse shapes in images, often generating undesirable artifacts or unnatural details. For this reason, combinations of various losses, such as perceptual, adversarial, and distortion losses, have been attempted, yet it remains challenging to find optimal combinations. Hence, in this paper, we propose a new SISR framework that applies optimal objectives for each region to generate plausible results in overall areas of high-resolution outputs. Specifically, the framework comprises two models: a predictive model that infers an optimal objective map for a given low-resolution (LR) input and a generative model that applies a target objective map to produce the corresponding SR output. The generative model is trained over our proposed objective trajectory representing a set of essential objectives, which enables the single network to learn various SR results corresponding to combined losses on the trajectory. The predictive model is trained using pairs of LR images and corresponding optimal objective maps searched from the objective trajectory. Experimental results on five benchmarks show that the proposed method outperforms state-of-the-art perception-driven SR methods in LPIPS, DISTS, PSNR, and SSIM metrics. The visual results also demonstrate the superiority of our method in perception-oriented reconstruction. The code and models are available at https://github.com/seungho-snu/SROOE.

最近的研究通过卷积神经网络（CNNS）显着提高了单图像超分辨率（SR）的性能。虽然可以有许多用于给定输入的高分辨率（HR）解决方案，但大多数现有的基于CNN的方法在推理期间不会探索替代解决方案。获得替代SR结果的典型方法是培训具有不同丢失权重的多个SR模型，并利用这些模型的组合。我们通过利用多任务学习，我们提出了一种更有效的方法来培训单个可调SR模型的单一可调SR模型。具体地，我们在训练期间优化具有条件目标的SR模型，其中目标是不同特征级别的多个感知损失的加权之和。权重根据给定条件而变化，并且该组重量被定义为样式控制器。此外，我们提出了一种适用于该训练方案的架构，该架构是配备有空间特征变换层的残留残余密集块。在推理阶段，我们培训的模型可以在样式控制地图上生成局部不同的输出。广泛的实验表明，所提出的SR模型在没有伪影的情况下产生各种所需的重建，并对最先进的SR方法产生相当的定量性能。

生成的对抗网络（GaN）中的发电机以粗到精细的方式学习图像生成，其中早期层学习图像的整体结构和后者细化细节。要播放粗略信息，最近的作品通常通过堆叠多个残差块来构建其发电机。虽然残余块可以产生高质量的图像以及稳定地训练，但它经常阻碍网络中的信息流。为了减轻这个问题，本简要介绍了一种新的发电机架构，通过组合通过两个不同的分支获得的特征来产生图像：主和辅助分支。主分支的目标是通过通过多个剩余块来产生图像，而辅助分支是将早期层中的粗略信息传送到稍后的块。要成功结合主和辅助分支机构中的功能，我们还提出了一个门控功能融合模块，用于控制这些分支机构中的信息流。为了证明所提出的方法的优越性，本简要提供了使用Cifar-10，CiFar-100，Lsun，Celeba-HQ，AFHQ和Tiny-ImageNet的各种标准数据集提供了广泛的实验。此外，我们进行了各种消融研究，以证明所提出的方法的泛化能力。定量评估证明，该方法在成立得分（IS）和FRECHET成立距离（FID）方面表现出令人印象深刻的GAN性能。例如，该方法可以分别提高FID，并分别在35.13至25.00和20.23至25.57之间的微小图像数据集上的分数。

本文介绍了一种新颖的卷积方法，称为生成卷积（GCONV），这对于改善生成的对抗网络（GaN）性能来说是简单而有效的。与标准卷积不同，GCONV首先选择与给定的潜像兼容的有用内核，然后线性地将所选内核结合起来制作潜在特定的内核。使用潜在特定的内核，所提出的方法产生潜在特定的特征，鼓励发电机产生高质量的图像。这种方法很简单，但令人惊讶地有效。首先，GaN性能随着额外的硬件成本而显着提高。其次，GCONV可以用于现有的最先进的发电机而不修改网络架构。为了揭示GCONV的优越性，本文使用各种标准数据集提供了广泛的实验，包括CiFar-10，CiFar-100，Lsun-Church，Celeba和微小想象成。定量评估证明，GCONV在成立得分（IS）和FRECHET成立距离（FID）方面大大提高了无条件和条件GAN的性能。例如，所提出的方法改善了FID，分别从35.13到29.76和20.23到22.64的微小想象网数据集上的分数。

本文提出了一种新颖的卷积层，称为扰动卷积（PCONV），该层侧重于同时实现两个目标：改善生成的对抗网络（GaN）性能并减轻判断者将所有图像从给定数据集记住的记忆问题，因为培训进步。在PCONV中，通过在执行卷积操作之前随机扰乱输入张量来产生扰动特征。这种方法很简单，但令人惊讶地有效。首先，为了产生类似的输出，即使使用扰动的张量，鉴别器中的每层也应该学习具有小本地嘴唇尖端值的鲁棒特征。其次，由于输入张量在培训过程中随机扰乱了神经网络中的辍学时，可以减轻记忆问题。为了展示所提出的方法的泛化能力，我们对各种丢失函数和数据集进行了广泛的实验，包括CIFAR-10，Celeba，Celeba-HQ，LSUN和微型想象成。定量评估表明，在FRECHET成立距离（FID）方面，PCONV有效地提高了GaN和条件GaN的性能。

The automated segmentation and tracking of macrophages during their migration are challenging tasks due to their dynamically changing shapes and motions. This paper proposes a new algorithm to achieve automatic cell tracking in time-lapse microscopy macrophage data. First, we design a segmentation method employing space-time filtering, local Otsu's thresholding, and the SUBSURF (subjective surface segmentation) method. Next, the partial trajectories for cells overlapping in the temporal direction are extracted in the segmented images. Finally, the extracted trajectories are linked by considering their direction of movement. The segmented images and the obtained trajectories from the proposed method are compared with those of the semi-automatic segmentation and manual tracking. The proposed tracking achieved 97.4% of accuracy for macrophage data under challenging situations, feeble fluorescent intensity, irregular shapes, and motion of macrophages. We expect that the automatically extracted trajectories of macrophages can provide pieces of evidence of how macrophages migrate depending on their polarization modes in the situation, such as during wound healing.

Data-centric AI has shed light on the significance of data within the machine learning (ML) pipeline. Acknowledging its importance, various research and policies are suggested by academia, industry, and government departments. Although the capability of utilizing existing data is essential, the capability to build a dataset has become more important than ever. In consideration of this trend, we propose a "Data Management Operation and Recipes" that will guide the industry regardless of the task or domain. In other words, this paper presents the concept of DMOps derived from real-world experience. By offering a baseline for building data, we want to help the industry streamline its data operation optimally.

According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms.

This paper proposes a new regularization algorithm referred to as macro-block dropout. The overfitting issue has been a difficult problem in training large neural network models. The dropout technique has proven to be simple yet very effective for regularization by preventing complex co-adaptations during training. In our work, we define a macro-block that contains a large number of units from the input to a Recurrent Neural Network (RNN). Rather than applying dropout to each unit, we apply random dropout to each macro-block. This algorithm has the effect of applying different drop out rates for each layer even if we keep a constant average dropout rate, which has better regularization effects. In our experiments using Recurrent Neural Network-Transducer (RNN-T), this algorithm shows relatively 4.30 % and 6.13 % Word Error Rates (WERs) improvement over the conventional dropout on LibriSpeech test-clean and test-other. With an Attention-based Encoder-Decoder (AED) model, this algorithm shows relatively 4.36 % and 5.85 % WERs improvement over the conventional dropout on the same test sets.

Affect understanding capability is essential for social robots to autonomously interact with a group of users in an intuitive and reciprocal way. However, the challenge of multi-person affect understanding comes from not only the accurate perception of each user's affective state (e.g., engagement) but also the recognition of the affect interplay between the members (e.g., joint engagement) that presents as complex, but subtle, nonverbal exchanges between them. Here we present a novel hybrid framework for identifying a parent-child dyad's joint engagement by combining a deep learning framework with various video augmentation techniques. Using a dataset of parent-child dyads reading storybooks together with a social robot at home, we first train RGB frame- and skeleton-based joint engagement recognition models with four video augmentation techniques (General Aug, DeepFake, CutOut, and Mixed) applied datasets to improve joint engagement classification performance. Second, we demonstrate experimental results on the use of trained models in the robot-parent-child interaction context. Third, we introduce a behavior-based metric for evaluating the learned representation of the models to investigate the model interpretability when recognizing joint engagement. This work serves as the first step toward fully unlocking the potential of end-to-end video understanding models pre-trained on large public datasets and augmented with data augmentation and visualization techniques for affect recognition in the multi-person human-robot interaction in the wild.