自主代理可以在新环境中导航而不构建明确的地图吗？对于PointGoal Navigation的任务（“转到$ \ delta x $，$ \ delta y $'），在理想化的设置（否RGB -D和驱动噪声，完美的GPS+Compass）下，答案是一个明确的“是” - 由任务无形组件（CNNS和RNN）组成的无地图神经模型接受了大规模增强学习训练，在标准数据集（Gibson）上取得了100％的成功。但是，对于PointNav在现实环境中（RGB-D和致动噪声，没有GPS+Compass），这是一个悬而未决的问题。我们在本文中解决了一个。该任务的最强成绩是成功的71.7％。首先，我们确定了性能下降的主要原因：GPS+指南针的缺失。带有RGB-D传感和致动噪声的完美GPS+指南针的代理商取得了99.8％的成功（Gibson-V2 Val）。这表明（解释模因）强大的视觉探子仪是我们对逼真的PointNav所需的全部。如果我们能够实现这一目标，我们可以忽略感应和致动噪声。作为我们的操作假设，我们扩展了数据集和模型大小，并开发了无人批准的数据启发技术来训练模型以进行视觉探测。我们在栖息地现实的PointNAV挑战方面的最新状态从71％降低到94％的成功（+23，31％相对）和53％至74％的SPL（+21，40％相对）。虽然我们的方法不饱和或“解决”该数据集，但这种强大的改进与有希望的零射击SIM2REAL转移（到Locobot）相结合提供了与假设一致的证据，即即使在现实环境中，显式映射也不是必需的。 。

We present a novel dataset named as HPointLoc, specially designed for exploring capabilities of visual place recognition in indoor environment and loop detection in simultaneous localization and mapping. The loop detection sub-task is especially relevant when a robot with an on-board RGB-D camera can drive past the same place (``Point") at different angles. The dataset is based on the popular Habitat simulator, in which it is possible to generate photorealistic indoor scenes using both own sensor data and open datasets, such as Matterport3D. To study the main stages of solving the place recognition problem on the HPointLoc dataset, we proposed a new modular approach named as PNTR. It first performs an image retrieval with the Patch-NetVLAD method, then extracts keypoints and matches them using R2D2, LoFTR or SuperPoint with SuperGlue, and finally performs a camera pose optimization step with TEASER++. Such a solution to the place recognition problem has not been previously studied in existing publications. The PNTR approach has shown the best quality metrics on the HPointLoc dataset and has a high potential for real use in localization systems for unmanned vehicles. The proposed dataset and framework are publicly available: https://github.com/metra4ok/HPointLoc.

Unmanned aerial vehicles (UAVs) mobility enables flexible and customized federated learning (FL) at the network edge. However, the underlying uncertainties in the aerial-terrestrial wireless channel may lead to a biased FL model. In particular, the distribution of the global model and the aggregation of the local updates within the FL learning rounds at the UAVs are governed by the reliability of the wireless channel. This creates an undesirable bias towards the training data of ground devices with better channel conditions, and vice versa. This paper characterizes the global bias problem of aerial FL in large-scale UAV networks. To this end, the paper proposes a channel-aware distribution and aggregation scheme to enforce equal contribution from all devices in the FL training as a means to resolve the global bias problem. We demonstrate the convergence of the proposed method by experimenting with the MNIST dataset and show its superiority compared to existing methods. The obtained results enable system parameter tuning to relieve the impact of the aerial channel deficiency on the FL convergence rate.

Despite recent progress towards scaling up multimodal vision-language models, these models are still known to struggle on compositional generalization benchmarks such as Winoground. We find that a critical component lacking from current vision-language models is relation-level alignment: the ability to match directional semantic relations in text (e.g., "mug in grass") with spatial relationships in the image (e.g., the position of the mug relative to the grass). To tackle this problem, we show that relation alignment can be enforced by encouraging the directed language attention from 'mug' to 'grass' (capturing the semantic relation 'in') to match the directed visual attention from the mug to the grass. Tokens and their corresponding objects are softly identified using the cross-modal attention. We prove that this notion of soft relation alignment is equivalent to enforcing congruence between vision and language attention matrices under a 'change of basis' provided by the cross-modal attention matrix. Intuitively, our approach projects visual attention into the language attention space to calculate its divergence from the actual language attention, and vice versa. We apply our Cross-modal Attention Congruence Regularization (CACR) loss to UNITER and improve on the state-of-the-art approach to Winoground.

A household robot should be able to navigate to target locations without requiring users to first annotate everything in their home. Current approaches to this object navigation challenge do not test on real robots and rely on expensive semantically labeled 3D meshes. In this work, our aim is an agent that builds self-supervised models of the world via exploration, the same as a child might. We propose an end-to-end self-supervised embodied agent that leverages exploration to train a semantic segmentation model of 3D objects, and uses those representations to learn an object navigation policy purely from self-labeled 3D meshes. The key insight is that embodied agents can leverage location consistency as a supervision signal - collecting images from different views/angles and applying contrastive learning to fine-tune a semantic segmentation model. In our experiments, we observe that our framework performs better than other self-supervised baselines and competitively with supervised baselines, in both simulation and when deployed in real houses.

Quantum machine learning techniques are commonly considered one of the most promising candidates for demonstrating practical quantum advantage. In particular, quantum kernel methods have been demonstrated to be able to learn certain classically intractable functions efficiently if the kernel is well-aligned with the target function. In the more general case, quantum kernels are known to suffer from exponential "flattening" of the spectrum as the number of qubits grows, preventing generalization and necessitating the control of the inductive bias by hyperparameters. We show that the general-purpose hyperparameter tuning techniques proposed to improve the generalization of quantum kernels lead to the kernel becoming well-approximated by a classical kernel, removing the possibility of quantum advantage. We provide extensive numerical evidence for this phenomenon utilizing multiple previously studied quantum feature maps and both synthetic and real data. Our results show that unless novel techniques are developed to control the inductive bias of quantum kernels, they are unlikely to provide a quantum advantage on classical data.

Pretrained language models have demonstrated extraordinary capabilities in language generation. However, real-world tasks often require controlling the distribution of generated text in order to mitigate bias, promote fairness, and achieve personalization. Existing techniques for controlling the distribution of generated text only work with quantified distributions, which require pre-defined categories, proportions of the distribution, or an existing corpus following the desired distributions. However, many important distributions, such as personal preferences, are unquantified. In this work, we tackle the problem of generating text following arbitrary distributions (quantified and unquantified) by proposing Nano, a few-shot human-in-the-loop training algorithm that continuously learns from human feedback. Nano achieves state-of-the-art results on single topic/attribute as well as quantified distribution control compared to previous works. We also show that Nano is able to learn unquantified distributions, achieves personalization, and captures differences between different individuals' personal preferences with high sample efficiency.

新颖的对象字幕（NOC）旨在描述包含对象的图像，而无需在训练过程中观察其地面真相标题。由于缺乏字幕注释，无法通过序列到序列训练或苹果酒优化直接优化字幕模型。结果，我们提出了启用释义（P2C），这是一个针对NOC的两阶段学习框架，它将通过释义通过释义来优化输出字幕。使用P2C，字幕模型首先从仅在文本语料库中预先训练的语言模型中学习释义，从而扩展了Bank一词以提高语言流利度。为了进一步实施足够描述输入图像的视觉内容的输出字幕，我们对引入的忠诚度和充分性目标进行字幕模型执行自我贴形。由于在训练过程中没有任何地面真相标题可用于新颖的对象图像，因此我们的P2C利用交叉模式（图像文本）关联模块可以确保可以正确保留上述字幕特征。在实验中，我们不仅表明我们的P2C在NOCAPS和COCO字幕数据集上实现了最先进的性能，而且还通过替换NOC的语言和跨模式关联模型来验证学习框架的有效性和灵活性。实施详细信息和代码可在补充材料中找到。

尽管学习环境内部模型的强化学习（RL）方法具有比没有模型的对应物更有效的样本效率，但学会从高维传感器中建模原始观察结果可能具有挑战性。先前的工作通过通过辅助目标（例如重建或价值预测）学习观察值的低维表示来解决这一挑战。但是，这些辅助目标与RL目标之间的一致性通常不清楚。在这项工作中，我们提出了一个单一的目标，该目标共同优化了潜在空间模型和政策，以实现高回报，同时保持自洽。这个目标是预期收益的下限。与基于模型的RL在策略探索或模型保证方面的先前范围不同，我们的界限直接依靠整体RL目标。我们证明，所得算法匹配或改善了最佳基于模型和无模型的RL方法的样品效率。尽管这种有效的样品方法通常在计算上是要求的，但我们的方法在较小的壁式锁定时间降低了50 \％。

多词表达式（MWE）是一系列单词，共同提出的含义不是从其单个单词中得出的。处理MWE的任务在许多自然语言处理（NLP）应用中至关重要，包括机器翻译和术语提取。因此，在不同领域中检测MWE是一个重要的研究主题。在本文中，我们探索了最新的神经变压器，以检测花和植物名称中的MWES。我们在由植物和花朵百科全书创建的数据集上评估了不同的变压器模型。我们从经验上表明，Transformer模型模型优于基于长期记忆（LSTM）的先前神经模型。