Inspired by the impressive performance of recent face image editing methods, several studies have been naturally proposed to extend these methods to the face video editing task. One of the main challenges here is temporal consistency among edited frames, which is still unresolved. To this end, we propose a novel face video editing framework based on diffusion autoencoders that can successfully extract the decomposed features - for the first time as a face video editing model - of identity and motion from a given video. This modeling allows us to edit the video by simply manipulating the temporally invariant feature to the desired direction for the consistency. Another unique strength of our model is that, since our model is based on diffusion models, it can satisfy both reconstruction and edit capabilities at the same time, and is robust to corner cases in wild face videos (e.g. occluded faces) unlike the existing GAN-based methods.

有条件图像生成的最新方法受益于密集的监督，例如分割标签图，以实现高保真性。但是，很少探索使用密集的监督进行无条件的图像生成。在这里，我们探讨了密集监督在无条件生成中的功效，找到生成器特征图可以替代成本昂贵的语义标签图。从我们的经验证据来看，我们提出了一种新的生成器引导的鉴别剂正则化（GGDR），其中生成器的特征地图监督了歧视者在无条件生成中具有丰富的语义表示。具体而言，我们采用了一个U-NET架构进行鉴别器，该体系结构经过训练，可以预测发电机特征图作为输入的伪造图像。关于Mulitple数据集的广泛实验表明，我们的GGDR始终在定量和定性方面提高基线方法的性能。代码可从https://github.com/naver-ai/ggdr获得

最近的研究表明，基于隐式神经表示（INR），gan的进展显着，而MLP鉴于其（x，y）坐标，该MLP产生RGB值。它们代表图像作为基础2D信号的连续版本，而不是2D阵列的像素，它为GAN应用程序打开了新的范围（例如，零击的超分辨率，图像支出）。但是，培训现有方法需要与图像分辨率成正比的重型计算成本，因为它们为每个（x，y）坐标都计算MLP操作。为了减轻此问题，我们提出了一种基于多阶段的培训，这是一种新颖且可扩展的方法，可以训练基于INR的gan具有灵活的计算成本，而不论图像分辨率如何。具体而言，我们的方法允许通过补丁产生和歧视图像的本地细节，并通过新颖的重建损失来学习全球结构信息，以实现有效的GAN培训。我们在几个基准数据集上进行实验，以证明我们的方法可以增强GPU内存中的基线模型，同时将FID保持在合理的水平。

实时视频细分是许多实际应用程序（例如自动驾驶和机器人控制）的关键任务。由于最新的语义细分模型尽管表现令人印象深刻，但对于实时应用来说通常太重了，因此研究人员提出了具有速度准确性权衡的轻量级体系结构，以降低准确性为代价实现实时速度。在本文中，我们提出了一个新颖的框架，通过利用视频中的时间位置来加快使用跳过连接进行实时视觉任务的架构。具体而言，在每个帧的到来时，我们将特征从上一个帧转换为在特定的空间箱中重复使用它们。然后，我们在当前帧区域上对骨干网络进行部分计算，以捕获当前帧和上一个帧之间的时间差异。这是通过使用门控机制动态掉出残留块来完成的，该机制决定哪些基于框架间失真掉落。我们在具有多个骨干网络的视频语义分割基准上验证了我们的时空掩码发生器（STMG），并证明我们的方法在很大程度上可以随着准确性的最小损失而加快推断。

在发现PLACE单元格之后，HPCAPPAL（HPC）函数的想法已经扩展到预测，想象力和概念认知地图。最近的研究争论HPC代表预测地图;并且已经表明，HPC预测对特定位置的访问。这种预测地图理论基于加强学习的继承代表（SR）。基于特征的SR（SF），它使用神经网络作为学习SR的函数近似，似乎更合理的神经生物学模型。然而，尚不众知的重量（W）初始化如何影响SF学习。在本研究中，SF学习者接触到简单的迷宫环境，以分析SF学习效率，并且W模式模式变化。使用了三种W初始化模式：标识矩阵，零矩阵和小随机矩阵。使用随机重量矩阵启动的SF学习者显示出比其他三个RL代理更好的性能。我们将讨论SF重量矩阵的神经生物学含义。通过这种方法，本文试图增加我们对神经科学和人工智能视角的智力的理解。

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.