Recently, many neural network-based image compression methods have shown promising results superior to the existing tool-based conventional codecs. However, most of them are often trained as separate models for different target bit rates, thus increasing the model complexity. Therefore, several studies have been conducted for learned compression that supports variable rates with single models, but they require additional network modules, layers, or inputs that often lead to complexity overhead, or do not provide sufficient coding efficiency. In this paper, we firstly propose a selective compression method that partially encodes the latent representations in a fully generalized manner for deep learning-based variable-rate image compression. The proposed method adaptively determines essential representation elements for compression of different target quality levels. For this, we first generate a 3D importance map as the nature of input content to represent the underlying importance of the representation elements. The 3D importance map is then adjusted for different target quality levels using importance adjustment curves. The adjusted 3D importance map is finally converted into a 3D binary mask to determine the essential representation elements for compression. The proposed method can be easily integrated with the existing compression models with a negligible amount of overhead increase. Our method can also enable continuously variable-rate compression via simple interpolation of the importance adjustment curves among different quality levels. The extensive experimental results show that the proposed method can achieve comparable compression efficiency as those of the separately trained reference compression models and can reduce decoding time owing to the selective compression. The sample codes are publicly available at https://github.com/JooyoungLeeETRI/SCR.

深度图用于从3D渲染到2D图像效应（例如散景）的广泛应用。但是，单个图像深度估计（侧）模型预测的人通常无法捕获对象中的孤立孔和/或具有不准确的边界区域。同时，使用商业自动掩蔽工具或现成的分割和垫子的方法，甚至是通过手动编辑，使用商业自动掩盖工具或现成的方法更容易获得。因此，在本文中，我们提出了一个新的掩盖引导深度细化的问题，该问题利用通用掩模来完善侧面模型的深度预测。我们的框架执行了分层的细化和介入/架设，将深度图分解为两个由掩码和倒置面罩表示的单独的层。由于具有深度和掩码注释的数据集很少，因此我们提出了一种使用任意掩码和RGB-D数据集的自我监督学习方案。我们从经验上表明，我们的方法对不同类型的掩模和初始深度预测具有鲁棒性，可以准确地完善内部和外掩模边界区域的深度值。我们通过消融研究进一步分析了我们的模型，并证明了实际应用的结果。可以在https://sooyekim.github.io/maskdepth/上找到更多信息。

在本文中，我们提出了一种新颖的联合去钻头和多帧插值（DEMFI）框架，称为DEMFI-NET，该网球被准确地将较低帧速率的模糊视频以基于流动引导的更高帧速率转换为尖锐的视频基于关提性的相关性的特征借助于多帧插值（MFI）的借助于基于相关的特征Bolstering（FAC-FB）模块和递归升压（RB）。 DEMFI-NET联合执行DeBlurring和MFI，其中其基线版本执行与FAC-FB模块的基于特征流的翘曲，以获得尖锐插值的帧，也可以解置两个中心输入帧。此外，其扩展版本进一步提高了基于基于像素的RB的像素流的翘曲的联合任务性能。我们的FAC-FB模块在特征域中的模糊输入帧中有效地聚集了分布式模糊像素信息，以改善整体关节性能，这是计算上有效的，因为其细心的相关性仅聚焦。结果，与最近的SOTA方法相比，我们的DEMFI-Net实现了最先进的数据集，用于近期SOTA方法，用于脱孔和MFI。所有源代码包括预押德福网在https://github.com/jihyongoh/demfi上公开提供。

尽管深度学习使图像介绍方面取得了巨大的飞跃，但当前的方法通常无法综合现实的高频细节。在本文中，我们建议将超分辨率应用于粗糙的重建输出，以高分辨率进行精炼，然后将输出降低到原始分辨率。通过将高分辨率图像引入改进网络，我们的框架能够重建更多的细节，这些细节通常由于光谱偏置而被平滑 - 神经网络倾向于比高频更好地重建低频。为了协助培训大型高度孔洞的改进网络，我们提出了一种渐进的学习技术，其中缺失区域的大小随着培训的进行而增加。我们的缩放，完善和缩放策略，结合了高分辨率的监督和渐进学习，构成了一种框架 - 不合时宜的方法，用于增强高频细节，可应用于任何基于CNN的涂层方法。我们提供定性和定量评估以及消融分析，以显示我们方法的有效性。这种看似简单但功能强大的方法优于最先进的介绍方法。我们的代码可在https://github.com/google/zoom-to-inpaint中找到

The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called Class-Continuous Conditional Generative NeRF ($\text{C}^{3}$G-NeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed $\text{C}^{3}$G-NeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, $\text{C}^{3}$G-NeRF exhibits a Fr\'echet Inception Distance (FID) of 7.64 in 3D-aware face image synthesis with a $\text{128}^{2}$ resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\text{C}^{3}$G-NeRF.

In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly being replaced by individual re-identification using image analysis. This paper introduces a contrastive learning-based model for identifying individuals. The model uses the first parts of the Inception v3 network, supported by a projection head, and we use contrastive learning to find similar or dissimilar image pairs from a collection of uniform photographs. We apply this technique for corkwing wrasse, Symphodus melops, an ecologically and commercially important fish species. Photos are taken during repeated catches of the same individuals from a wild population, where the intervals between individual sightings might range from a few days to several years. Our model achieves a one-shot accuracy of 0.35, a 5-shot accuracy of 0.56, and a 100-shot accuracy of 0.88, on our dataset.

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.

The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.

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.

In robotics and computer vision communities, extensive studies have been widely conducted regarding surveillance tasks, including human detection, tracking, and motion recognition with a camera. Additionally, deep learning algorithms are widely utilized in the aforementioned tasks as in other computer vision tasks. Existing public datasets are insufficient to develop learning-based methods that handle various surveillance for outdoor and extreme situations such as harsh weather and low illuminance conditions. Therefore, we introduce a new large-scale outdoor surveillance dataset named eXtremely large-scale Multi-modAl Sensor dataset (X-MAS) containing more than 500,000 image pairs and the first-person view data annotated by well-trained annotators. Moreover, a single pair contains multi-modal data (e.g. an IR image, an RGB image, a thermal image, a depth image, and a LiDAR scan). This is the first large-scale first-person view outdoor multi-modal dataset focusing on surveillance tasks to the best of our knowledge. We present an overview of the proposed dataset with statistics and present methods of exploiting our dataset with deep learning-based algorithms. The latest information on the dataset and our study are available at https://github.com/lge-robot-navi, and the dataset will be available for download through a server.