最近的生成模型的成功表明，利用多模态嵌入空间可以使用文本信息操纵图像。然而，由于源的动态特性，使用其他来源而不是声音的文本来操纵图像，而不是声音，并不容易。特别是，声音可以传达真实世界的生动情感和动态表达。在这里，我们提出了一个框架，该框架将声音直接编码为多模态（图像文本）嵌入空间，并从空间操纵图像。我们的音频编码器受过培训以产生来自音频输入的潜在表示，该音频输入被强制与多模式嵌入空间中的图像和文本表示对齐。我们使用基于对齐的嵌入式的直接潜在优化方法进行声音引导图像操纵。我们还表明，我们的方法可以混合文本和音频模态，这丰富了各种图像修改。我们验证了定量和定性的声音引导图像操纵的有效性。我们还表明，我们的方法可以混合不同的模态，即文本和音频，这丰富了图像修改的各种。零射频分类和语义级图像分类的实验表明，我们所提出的模型优于其他文本和声音引导最先进的方法。

Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node. In this work, we formally introduce the notion of neighborhood fairness and develop a computational framework for learning such locally fair embeddings. We argue that the notion of neighborhood fairness is more appropriate since GNN-based models operate at the local neighborhood level of a node. Our neighborhood fairness framework has two main components that are flexible for learning fair graph representations from arbitrary data: the first aims to construct fair neighborhoods for any arbitrary node in a graph and the second enables adaption of these fair neighborhoods to better capture certain application or data-dependent constraints, such as allowing neighborhoods to be more biased towards certain attributes or neighbors in the graph.Furthermore, while link prediction has been extensively studied, we are the first to investigate the graph representation learning task of fair link classification. We demonstrate the effectiveness of the proposed neighborhood fairness framework for a variety of graph machine learning tasks including fair link prediction, link classification, and learning fair graph embeddings. Notably, our approach achieves not only better fairness but also increases the accuracy in the majority of cases across a wide variety of graphs, problem settings, and metrics.

Point-of-Care Ultrasound (POCUS) refers to clinician-performed and interpreted ultrasonography at the patient's bedside. Interpreting these images requires a high level of expertise, which may not be available during emergencies. In this paper, we support POCUS by developing classifiers that can aid medical professionals by diagnosing whether or not a patient has pneumothorax. We decomposed the task into multiple steps, using YOLOv4 to extract relevant regions of the video and a 3D sparse coding model to represent video features. Given the difficulty in acquiring positive training videos, we trained a small-data classifier with a maximum of 15 positive and 32 negative examples. To counteract this limitation, we leveraged subject matter expert (SME) knowledge to limit the hypothesis space, thus reducing the cost of data collection. We present results using two lung ultrasound datasets and demonstrate that our model is capable of achieving performance on par with SMEs in pneumothorax identification. We then developed an iOS application that runs our full system in less than 4 seconds on an iPad Pro, and less than 8 seconds on an iPhone 13 Pro, labeling key regions in the lung sonogram to provide interpretable diagnoses.

We present a robust, privacy-preserving visual localization algorithm using event cameras. While event cameras can potentially make robust localization due to high dynamic range and small motion blur, the sensors exhibit large domain gaps making it difficult to directly apply conventional image-based localization algorithms. To mitigate the gap, we propose applying event-to-image conversion prior to localization which leads to stable localization. In the privacy perspective, event cameras capture only a fraction of visual information compared to normal cameras, and thus can naturally hide sensitive visual details. To further enhance the privacy protection in our event-based pipeline, we introduce privacy protection at two levels, namely sensor and network level. Sensor level protection aims at hiding facial details with lightweight filtering while network level protection targets hiding the entire user's view in private scene applications using a novel neural network inference pipeline. Both levels of protection involve light-weight computation and incur only a small performance loss. We thus project our method to serve as a building block for practical location-based services using event cameras. The code and dataset will be made public through the following link: https://github.com/82magnolia/event_localization.

Federated learning (FL) has emerged as a solution to deal with the risk of privacy leaks in machine learning training. This approach allows a variety of mobile devices to collaboratively train a machine learning model without sharing the raw on-device training data with the cloud. However, efficient edge deployment of FL is challenging because of the system/data heterogeneity and runtime variance. This paper optimizes the energy-efficiency of FL use cases while guaranteeing model convergence, by accounting for the aforementioned challenges. We propose FedGPO based on a reinforcement learning, which learns how to identify optimal global parameters (B, E, K) for each FL aggregation round adapting to the system/data heterogeneity and stochastic runtime variance. In our experiments, FedGPO improves the model convergence time by 2.4 times, and achieves 3.6 times higher energy efficiency over the baseline settings, respectively.

Recent 3D generative models have achieved remarkable performance in synthesizing high resolution photorealistic images with view consistency and detailed 3D shapes, but training them for diverse domains is challenging since it requires massive training images and their camera distribution information. Text-guided domain adaptation methods have shown impressive performance on converting the 2D generative model on one domain into the models on other domains with different styles by leveraging the CLIP (Contrastive Language-Image Pre-training), rather than collecting massive datasets for those domains. However, one drawback of them is that the sample diversity in the original generative model is not well-preserved in the domain-adapted generative models due to the deterministic nature of the CLIP text encoder. Text-guided domain adaptation will be even more challenging for 3D generative models not only because of catastrophic diversity loss, but also because of inferior text-image correspondence and poor image quality. Here we propose DATID-3D, a domain adaptation method tailored for 3D generative models using text-to-image diffusion models that can synthesize diverse images per text prompt without collecting additional images and camera information for the target domain. Unlike 3D extensions of prior text-guided domain adaptation methods, our novel pipeline was able to fine-tune the state-of-the-art 3D generator of the source domain to synthesize high resolution, multi-view consistent images in text-guided targeted domains without additional data, outperforming the existing text-guided domain adaptation methods in diversity and text-image correspondence. Furthermore, we propose and demonstrate diverse 3D image manipulations such as one-shot instance-selected adaptation and single-view manipulated 3D reconstruction to fully enjoy diversity in text.

We propose a domain adaptation method, MoDA, which adapts a pretrained embodied agent to a new, noisy environment without ground-truth supervision. Map-based memory provides important contextual information for visual navigation, and exhibits unique spatial structure mainly composed of flat walls and rectangular obstacles. Our adaptation approach encourages the inherent regularities on the estimated maps to guide the agent to overcome the prevalent domain discrepancy in a novel environment. Specifically, we propose an efficient learning curriculum to handle the visual and dynamics corruptions in an online manner, self-supervised with pseudo clean maps generated by style transfer networks. Because the map-based representation provides spatial knowledge for the agent's policy, our formulation can deploy the pretrained policy networks from simulators in a new setting. We evaluate MoDA in various practical scenarios and show that our proposed method quickly enhances the agent's performance in downstream tasks including localization, mapping, exploration, and point-goal navigation.

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

使用相对比心脏磁共振成像（PC-CMR）进行的流量分析可以量化用于评估心血管功能的重要参数。该分析的重要部分是鉴定正确的CMR视图和质量控制（QC），以检测可能影响流量定量的伪像。我们提出了一个新型的基于深度学习的框架，用于对完整CMR扫描的流量进行完全自动化的分析，该框架首先使用两个顺序卷积神经网络进行这些视图选择和QC步骤，然后进行自动主动脉和肺动脉分段，以实现对量化的量化。钥匙流参数。对于观察分类和QC，获得了0.958和0.914的精度值。对于细分，骰子分数为$> $ 0.969，而平淡的altman情节表示手动和自动峰流量值之间的一致性很高。此外，我们在外部验证数据集上测试了管道，结果表明管道的鲁棒性。这项工作是使用由986例病例组成的多生临床数据进行的，表明在临床环境中使用该管道的潜力。

磁共振图像的降解有益于提高低信噪比图像的质量。最近，使用深层神经网络进行DENOSING表现出了令人鼓舞的结果。但是，这些网络大多数都利用监督学习，这需要大量的噪声和清洁图像对的培训图像。获得训练图像，尤其是干净的图像，既昂贵又耗时。因此，已经开发了仅需要成对噪声浪费图像的噪声2Noise（N2N）之类的方法来减轻获得训练数据集的负担。在这项研究中，我们提出了一种新的自我监督的denoising方法Coil2Coil（C2C），该方法不需要获取干净的图像或配对的噪声浪费图像进行训练。取而代之的是，该方法利用了从分阶段阵列线圈中的多通道数据来生成训练图像。首先，它将多通道线圈图像分为两个图像，一个用于输入，另一个用于标签。然后，它们被处理以施加噪声独立性和敏感性归一化，以便它们可用于N2N的训练图像。为了推断，该方法输入了一个线圈组合的图像（例如DICOM图像），从而允许该方法的广泛应用。当使用合成噪声添加的图像进行评估时，C2C对几种自我监督方法显示了最佳性能，从而报告了与监督方法的可比结果。在测试DICOM图像时，C2C成功地将真实噪声降低，而没有显示误差图中的结构依赖性残差。由于不需要对清洁或配对图像进行额外扫描的显着优势，因此可以轻松地用于各种临床应用。