手语制作（SLP）旨在将语言的表达方式转化为手语的相应语言，例如基于骨架的标志姿势或视频。现有的SLP型号是自动回旋（AR）或非自动入口（NAR）。但是，AR-SLP模型在解码过程中遭受了回归对均值和误差传播的影响。 NSLP-G是一种基于NAR的模型，在某种程度上解决了这些问题，但会带来其他问题。例如，它不考虑目标符号长度，并且会遭受虚假解码启动的影响。我们通过知识蒸馏（KD）提出了一种新型的NAR-SLP模型，以解决这些问题。首先，我们设计一个长度调节器来预测生成的符号姿势序列的末端。然后，我们采用KD，该KD从预训练的姿势编码器中提取空间语言特征以减轻虚假解码的启动。广泛的实验表明，所提出的方法在特里切特的手势距离和背面翻译评估中都显着优于现有的SLP模型。

类激活图（CAM）有助于制定显着图，有助于解释深度神经网络的预测。基于梯度的方法通常比视力解释性的其他分支更快，并且独立于人类的指导。类似CAM的研究的性能取决于管理模型的层响应以及梯度的影响。典型的面向梯度的CAM研究依赖加权聚合来进行显着图估计，通过将梯度图投影到单权重值中，这可能导致过度的广义显着图。为了解决此问题，我们使用全球指导图来纠正显着性估计过程中加权聚合操作，在这种情况下，结果解释是相对干净的ER且特定于实例的。我们通过在特征图及其相应的梯度图之间执行元素乘法来获得全局引导图。为了验证我们的研究，我们将拟议的研究与八个不同的显着性可视化器进行了比较。此外，我们使用七个常用的评估指标进行定量比较。提出的方案比ImageNet，MS-Coco 14和Pascal VOC 2012数据集的测试图像取得了重大改进。

在许多计算机视觉子域中，图像降级仍然是一个具有挑战性的问题。最近的研究表明，在有监督的环境中取得了重大改进。但是，很少有挑战（例如空间忠诚度和类似卡通的平滑度）仍未解决或果断地忽略。我们的研究提出了一个简单而有效的架构，用于解决上述问题的降级问题。所提出的体系结构重新审视了模块化串联的概念，而不是长时间和更深的级联连接，以恢复给定图像的更清洁近似。我们发现不同的模块可以捕获多功能表示形式，而串联表示为低级图像恢复创造了更丰富的子空间。所提出的架构的参数数量仍然小于以前的大多数网络的数量，并且仍然对当前最新网络进行了重大改进。

gan中潜在空间的分离特性的发现促使许多研究找到了语义上有意义的方向。在本文中，我们建议解开特性与潜在空间的几何形状密切相关。在这方面，我们提出了一种基于局部几何形状在gan的中间潜在空间上找到语义因素的方法的无监督方法。直觉上，我们提出的方法称为局部基础，发现基本潜在变量附近的潜在空间的主要变化。实验结果表明，局部主变异对应于语义分解，并沿着它横穿它为图像遍历提供了强大的鲁棒性。此外，我们为在潜在空间（尤其是stylegan2的W-Space）中找到全球遍历方向的成功有限的解释。我们表明，W-Space通过比较当地的几何形状，通过Grassmannian歧管上的度量进行比较，通过比较当地的几何形状。全球扭曲意味着潜在空间在全球范围内不妥善调整，因此全球遍历方向必将显示出有限的成功。

最近，隐写术领域经历了基于深度学习（DL）的快速发展。基于DL的隐写术在封面图像的所有可用位分发了秘密信息，从而在使用传统的隐分方法来检测，提取或删除隐藏秘密图像的困难。但是，我们提出的框架是第一个有效禁用使用基于DL的隐写术的秘密通信和事务。我们提出了一种基于DL的隐分技术，其通过恢复原始图像的分布而有效地去除秘密图像。我们通过使用深神经网络利用复杂的像素分布和图像的边缘分布来制定问题并解决它。根据给定的信息，我们在像素级别删除隐藏的秘密信息。我们通过使用三个公共基准与传统的隐草方法进行比较来评估我们的技术。由于基于DL的隐写的解码方法是近似的（损失）并且与传统隐写术的解码方法不同，我们还引入了一种称为破坏率（DT）的新的定量度量。实验结果表明，在解码速率和DT中表现出10-20％的性能提高。

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.

Supervision for metric learning has long been given in the form of equivalence between human-labeled classes. Although this type of supervision has been a basis of metric learning for decades, we argue that it hinders further advances of the field. In this regard, we propose a new regularization method, dubbed HIER, to discover the latent semantic hierarchy of training data, and to deploy the hierarchy to provide richer and more fine-grained supervision than inter-class separability induced by common metric learning losses. HIER achieved this goal with no annotation for the semantic hierarchy but by learning hierarchical proxies in hyperbolic spaces. The hierarchical proxies are learnable parameters, and each of them is trained to serve as an ancestor of a group of data or other proxies to approximate the semantic hierarchy among them. HIER deals with the proxies along with data in hyperbolic space since geometric properties of the space are well-suited to represent their hierarchical structure. The efficacy of HIER was evaluated on four standard benchmarks, where it consistently improved performance of conventional methods when integrated with them, and consequently achieved the best records, surpassing even the existing hyperbolic metric learning technique, in almost all settings.

Steering language generation towards objectives or away from undesired content has been a long-standing goal in utilizing language models (LM). Recent work has demonstrated reinforcement learning and weighted decoding as effective approaches to achieve a higher level of language control and quality with pros and cons. In this work, we propose a novel critic decoding method for controlled language generation (CriticControl) that combines the strengths of reinforcement learning and weighted decoding. Specifically, we adopt the actor-critic framework to train an LM-steering critic from non-differentiable reward models. And similar to weighted decoding, our method freezes the language model and manipulates the output token distribution using called critic, improving training efficiency and stability. Evaluation of our method on three controlled generation tasks, namely topic control, sentiment control, and detoxification, shows that our approach generates more coherent and well-controlled texts than previous methods. In addition, CriticControl demonstrates superior generalization ability in zero-shot settings. Human evaluation studies also corroborate our findings.

Task-oriented dialogue (TOD) systems are mainly based on the slot-filling-based TOD (SF-TOD) framework, in which dialogues are broken down into smaller, controllable units (i.e., slots) to fulfill a specific task. A series of approaches based on this framework achieved remarkable success on various TOD benchmarks. However, we argue that the current TOD benchmarks are limited to surrogate real-world scenarios and that the current TOD models are still a long way from unraveling the scenarios. In this position paper, we first identify current status and limitations of SF-TOD systems. After that, we explore the WebTOD framework, the alternative direction for building a scalable TOD system when a web/mobile interface is available. In WebTOD, the dialogue system learns how to understand the web/mobile interface that the human agent interacts with, powered by a large-scale language model.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.