域的概括（DG）旨在仅使用有限的源域学习一个通用模型。先前的DG尝试仅由于训练和测试域之间的显着域移动而无法从源域中学习域不变表示。取而代之的是，我们使用Oracle模型使用共同信息重新构建了DG目标，该模型将概括为任何可能的域。我们通过通过预训练的模型近似oracle模型来得出一个可拖动的变化下限，称为使用Oracle（Miro）的相互信息正则化。我们的广泛实验表明，Miro可显着提高分布性能。此外，我们的缩放实验表明，预训练模型的尺度越大，miro的性能提高就越大。源代码可在https://github.com/kakaobrain/miro中获得。

通过提供语义来改进字符序列，语言知识对现场文本识别带来了很大的好处。然而，由于语言知识已经单独应用于输出序列，因此之前的方法没有充分利用语义来理解文本识别的视觉线索。本文介绍了一种名为多模态文本识别网络（MITRN）的新方法，其能够实现视觉和语义特征之间的相互作用以获得更好的识别性能。具体地，Matrn识别视觉和语义特征对并将空间信息进行编码为语义特征。基于空间编码，通过参考其他模态的相关特征提高了视觉和语义特征。此外，通过隐藏与训练阶段中的角色相关的视觉线程来刺激基质特征将语义特征组合成视觉特征。我们的实验表明，在具有大边缘的七个基准上实现了最先进的表演，而两个方式的天真组合显示了边缘改善。进一步消融研究证明了我们所提出的组件的有效性。我们的实施将公开提供。

域泛化（DG）方法旨在通过仅使用来自源域的训练数据来实现未经证明的目标域的概括性。虽然已经提出了各种DG方法，但最近的一项研究表明，在一个公平的评估方案下，称为域底，简单的经验风险最小化（ERM）方法可与以前的方法相当。不幸的是，简单地解决了ERM在复杂的非凸损函数上，可以通过寻求尖锐的最小值来容易地导致次优化的普遍性。在本文中，我们理论上表明发现扁平最小值导致较小的域泛化差距。我们还提出了一种简单而有效的方法，名为随机重量平均（纵向），找到扁平的最小值。瑞郎发现更漂亮的最小值，并且由于通过密集和过度感知的随机重量采样策略而遭受的过度装备不足。瑞士瑞士展示了五个DG基准测试，即PACS，VLC，OfficeHome，Terraincognita和Domainnet的最先进的表演，符合域名准确度的一致和大幅度+ 1.6％。我们还与常规的泛化方法（如数据增强和一致性正则化方法）进行比较，以验证显着的性能改进是通过寻求扁平的最小值，而不是更好的域概括性。最后但并非最不重要的是，瑞士剧本适应现有的DG方法而无需修改;施联和现有DG方法的组合进一步提高了DG性能。源代码可在https://github.com/khanrc/swad提供。

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.

Training agents via off-policy deep reinforcement learning (RL) requires a large memory, named replay memory, that stores past experiences used for learning. These experiences are sampled, uniformly or non-uniformly, to create the batches used for training. When calculating the loss function, off-policy algorithms assume that all samples are of the same importance. In this paper, we hypothesize that training can be enhanced by assigning different importance for each experience based on their temporal-difference (TD) error directly in the training objective. We propose a novel method that introduces a weighting factor for each experience when calculating the loss function at the learning stage. In addition to improving convergence speed when used with uniform sampling, the method can be combined with prioritization methods for non-uniform sampling. Combining the proposed method with prioritization methods improves sampling efficiency while increasing the performance of TD-based off-policy RL algorithms. The effectiveness of the proposed method is demonstrated by experiments in six environments of the OpenAI Gym suite. The experimental results demonstrate that the proposed method achieves a 33%~76% reduction of convergence speed in three environments and an 11% increase in returns and a 3%~10% increase in success rate for other three environments.

Neural fields, also known as coordinate-based or implicit neural representations, have shown a remarkable capability of representing, generating, and manipulating various forms of signals. For video representations, however, mapping pixel-wise coordinates to RGB colors has shown relatively low compression performance and slow convergence and inference speed. Frame-wise video representation, which maps a temporal coordinate to its entire frame, has recently emerged as an alternative method to represent videos, improving compression rates and encoding speed. While promising, it has still failed to reach the performance of state-of-the-art video compression algorithms. In this work, we propose FFNeRV, a novel method for incorporating flow information into frame-wise representations to exploit the temporal redundancy across the frames in videos inspired by the standard video codecs. Furthermore, we introduce a fully convolutional architecture, enabled by one-dimensional temporal grids, improving the continuity of spatial features. Experimental results show that FFNeRV yields the best performance for video compression and frame interpolation among the methods using frame-wise representations or neural fields. To reduce the model size even further, we devise a more compact convolutional architecture using the group and pointwise convolutions. With model compression techniques, including quantization-aware training and entropy coding, FFNeRV outperforms widely-used standard video codecs (H.264 and HEVC) and performs on par with state-of-the-art video compression algorithms.