最近的研究表明，基于梯度匹配的数据集综合或数据集凝结（DC），当应用于数据有效的学习任务时，方法可以实现最先进的性能。但是，在这项研究中，我们证明，当任务 - 核定信息构成培训数据集的重要组成部分时，现有的DC方法比随机选择方法的性能更糟。我们将其归因于缺乏与课堂梯度匹配策略所产生的类对比信号的参与。为了解决此问题，我们通过修改损耗函数以使DC方法有效地捕获类之间的差异来提出与对比度信号（DCC）的数据集凝结。此外，我们通过跟踪内核速度来分析训练动力学的新损失函数。此外，我们引入了双层热身策略，以稳定优化。我们的实验结果表明，尽管现有方法对细粒度的图像分类任务无效，但所提出的方法可以成功地为相同任务生成信息合成数据集。此外，我们证明所提出的方法甚至在基准数据集（例如SVHN，CIFAR-10和CIFAR-100）上也优于基准。最后，我们通过将其应用于持续学习任务来证明该方法的高度适用性。

最近，公平感知学习已经变得越来越重要，但我们注意到这些方法的大多数方法是通过假设完全注释的组标签的可用性来运作。我们强调，这种假设对于现实世界的应用是不现实的，因为组标签注释昂贵，并且可以与隐私问题冲突。在本文中，我们考虑了一种更实际的场景，称为算法公平，部分注释的组标签（Fair-PG）。我们观察到现有的公平方法，该方法仅使用与组标签的数据，表现比Vanilla培训更糟糕，这仅在Fair-PG下使用目标标签使用完整数据。为了解决这个问题，我们提出了一个简单的基于席信的群标签分配（CGL）策略，这些策略随时适用于任何公平意识的学习方法。我们的CGL利用辅助组分类器分配伪组标签，其中随机标签分配给低自信的样本。我们首先理论上表明，在公平标准方面，我们的方法设计优于香草伪标签策略。然后，我们经验展示了通过组合CGL和最先进的公平性的处理方法，与基线方法相比结合CGL和最先进的公平知识的处理方法，将目标精度和公平度量进行联合改善。此外，我们令人信服地表明，我们的CGL使得自然地将给定的组标记的数据集自然使用外部数据集仅适用于目标标签，以便可以提高精度和公平度量。我们将公开释放我们的实施，以便将来的研究重现我们的结果。

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.

Estimating the 6D pose of objects is one of the major fields in 3D computer vision. Since the promising outcomes from instance-level pose estimation, the research trends are heading towards category-level pose estimation for more practical application scenarios. However, unlike well-established instance-level pose datasets, available category-level datasets lack annotation quality and provided pose quantity. We propose the new category level 6D pose dataset HouseCat6D featuring 1) Multi-modality of Polarimetric RGB+P and Depth, 2) Highly diverse 194 objects of 10 household object categories including 2 photometrically challenging categories, 3) High-quality pose annotation with an error range of only 1.35 mm to 1.74 mm, 4) 41 large scale scenes with extensive viewpoint coverage, 5) Checkerboard-free environment throughout the entire scene. We also provide benchmark results of state-of-the-art category-level pose estimation networks.

An important challenge in vision-based action recognition is the embedding of spatiotemporal features with two or more heterogeneous modalities into a single feature. In this study, we propose a new 3D deformable transformer for action recognition with adaptive spatiotemporal receptive fields and a cross-modal learning scheme. The 3D deformable transformer consists of three attention modules: 3D deformability, local joint stride, and temporal stride attention. The two cross-modal tokens are input into the 3D deformable attention module to create a cross-attention token with a reflected spatiotemporal correlation. Local joint stride attention is applied to spatially combine attention and pose tokens. Temporal stride attention temporally reduces the number of input tokens in the attention module and supports temporal expression learning without the simultaneous use of all tokens. The deformable transformer iterates L times and combines the last cross-modal token for classification. The proposed 3D deformable transformer was tested on the NTU60, NTU120, FineGYM, and Penn Action datasets, and showed results better than or similar to pre-trained state-of-the-art methods even without a pre-training process. In addition, by visualizing important joints and correlations during action recognition through spatial joint and temporal stride attention, the possibility of achieving an explainable potential for action recognition is presented.

Recent studies have proposed a unified user modeling framework that leverages user behavior data from various applications. Most benefit from utilizing users' behavior sequences as plain texts, representing rich information in any domain or system without losing generality. Hence, a question arises: Can language modeling for user history corpus help improve recommender systems? While its versatile usability has been widely investigated in many domains, its applications to recommender systems still remain underexplored. We show that language modeling applied directly to task-specific user histories achieves excellent results on diverse recommendation tasks. Also, leveraging additional task-agnostic user histories delivers significant performance benefits. We further demonstrate that our approach can provide promising transfer learning capabilities for a broad spectrum of real-world recommender systems, even on unseen domains and services.

While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. Towards enabling QFL under various channel conditions, in this article we develop a depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs), and propose an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eS-QNNs. Compared to the existing depth-fixed QNNs, training the depth-controllable eSQNN architecture is more challenging due to high entanglement entropy and inter-depth interference, which are mitigated by introducing entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing inter-depth quantum state differences, respectively. Furthermore, we optimize the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. In an image classification task, extensive simulations corroborate the effectiveness of eSQFL in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions.