基于医学图像（例如X射线图像）的诊断通常涉及解剖关键的手动注释。但是，这个过程涉及大量的人类努力，因此可以成为诊断过程中的瓶颈。为了充分自动化此过程，基于深度学习的方法已被广泛提出，并在检测医学图像中的关键点方面达到了高性能。但是，这些方法仍然存在临床局限性：无法保证所有情况的准确性，并且医生必须对所有模型的所有预测进行仔细检查。作为回应，我们提出了一个新颖的深神经网络，鉴于X射线图像，它可以通过用户相互作用的系统自动检测和完善解剖学关键点，在该系统中，医生可以以比手动修订过程中所需的点击率更少的点击量来修复错误预测的关键。使用我们自己的收集数据和公开可用的AASCE数据集，我们证明了该方法通过广泛的定量和定性结果来降低注释成本的有效性。我们的项目网页上提供了有关我们方法的演示视频。

由于发型的复杂性和美味，编辑发型是独一无二的，而且具有挑战性。尽管最近的方法显着改善了头发的细节，但是当源图像的姿势与目标头发图像的姿势大不相同时，这些模型通常会产生不良的输出，从而限制了其真实世界的应用。发型是一种姿势不变的发型转移模型，可以减轻这种限制，但在保留精致的头发质地方面仍然表现出不令人满意的质量。为了解决这些局限性，我们提出了配备潜在优化和新呈现的局部匹配损失的高性能姿势不变的发型转移模型。在stylegan2潜在空间中，我们首先探索目标头发的姿势对准的潜在代码，并根据本地风格匹配保留了详细纹理。然后，我们的模型对源的遮挡构成了对齐的目标头发的遮挡，并将两个图像混合在一起以产生最终输出。实验结果表明，我们的模型在在较大的姿势差异和保留局部发型纹理下转移发型方面具有优势。

发型转移是将源发型修改为目标的任务。尽管最近的发型转移模型可以反映发型的精致特征，但它们仍然有两个主要局限性。首先，当源和目标图像具有不同的姿势（例如，查看方向或面部尺寸）时，现有方法无法转移发型，这在现实世界中很普遍。同样，当源图像中有非平凡的区域被其原始头发遮住时，先前的模型会产生不切实际的图像。当将长发修改为短发时，肩膀或背景被长发遮住了。为了解决这些问题，我们为姿势不变的发型转移，发型提出了一个新颖的框架。我们的模型包括两个阶段：1）基于流动的头发对齐和2）头发合成。在头发对齐阶段，我们利用基于关键点的光流估计器将目标发型与源姿势对齐。然后，我们基于语义区域感知的嵌入面膜（SIM）估计器在头发合成阶段生成最终的发型转移图像。我们的SIM估计器将源图像中的封闭区域划分为不同的语义区域，以反映其在涂料过程中的独特特征。为了证明我们的模型的有效性，我们使用多视图数据集（K-Hairstyle和Voxceleb）进行定量和定性评估。结果表明，发型通过在不同姿势的图像之间成功地转移发型来实现最先进的表现，而这是以前从未实现的。

视频去抑制是一种高度均不存在的问题，因为模糊劣化过程中的运动信息丢失。由于事件相机可以通过高时分辨率捕获表观运动，因此多次尝试探索了引导视频去纹的事件的潜力。这些方法通常认为曝光时间与视频帧速率的倒数相同。然而，在实际情况下，这不是真的，并且曝光时间可能是未知的并且根据视频拍摄环境（例如，照明条件）动态地变化。在本文中，假设基于帧的相机的动态可变未知的曝光时间来解决事件引导视频去纹。为此，我们首先通过考虑视频帧采集过程中的曝光和读出时间来推导出事件引导视频去掩模的新配方。然后，我们提出了一种用于事件引导视频去纹的新的结束终端学习框架。特别地，我们设计了一种新的基于曝光时间的事件选择（ETES）模块，以通过估计来自模糊帧和事件的特征之间的跨模型相关来选择性地使用事件特征。此外，我们提出了一种特征融合模块，以有效地熔断来自事件和模糊帧的所选功能。我们对各种数据集进行广泛的实验，并证明我们的方法实现了最先进的性能。我们的项目代码和预付费型号将可用。

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.