The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

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.

We study the problem of synthesizing immersive 3D indoor scenes from one or more images. Our aim is to generate high-resolution images and videos from novel viewpoints, including viewpoints that extrapolate far beyond the input images while maintaining 3D consistency. Existing approaches are highly complex, with many separately trained stages and components. We propose a simple alternative: an image-to-image GAN that maps directly from reprojections of incomplete point clouds to full high-resolution RGB-D images. On the Matterport3D and RealEstate10K datasets, our approach significantly outperforms prior work when evaluated by humans, as well as on FID scores. Further, we show that our model is useful for generative data augmentation. A vision-and-language navigation (VLN) agent trained with trajectories spatially-perturbed by our model improves success rate by up to 1.5% over a state of the art baseline on the R2R benchmark. Our code will be made available to facilitate generative data augmentation and applications to downstream robotics and embodied AI tasks.

个性化对话的产生对于自然和人类的谈话至关重要。通常，个性化对话生成模型涉及对对话历史的生成响应以及对话者的角色/人格的表示。由于获得每个对话者的人格/人格表征是不切实际的，最近的作品已经探讨了通过将模型与对应于给定的人格对应的对话示例的模型来产生个性化对话的可能性。然而，在实际实现中，足够数量的相应对话示例也很少可用。因此，在本文中，我们提出了一种能够在没有任何人格/人格信息或任何相应的对话示例的情况下产生个性化对话的双潜变量发生器（DLVGEN）。与现有工作不同，DLVGEN模拟潜在响应的潜在分布以及代理商的潜在角色的潜在分布。在推理期间，从两个分布中采样潜在变量并进入解码器。经验结果表明，DLVGEN能够产生各种反应，精确地纳入代理人的角色。

This paper presents The Shared Task on Euphemism Detection for the Third Workshop on Figurative Language Processing (FigLang 2022) held in conjunction with EMNLP 2022. Participants were invited to investigate the euphemism detection task: given input text, identify whether it contains a euphemism. The input data is a corpus of sentences containing potentially euphemistic terms (PETs) collected from the GloWbE corpus (Davies and Fuchs, 2015), and are human-annotated as containing either a euphemistic or literal usage of a PET. In this paper, we present the results and analyze the common themes, methods and findings of the participating teams

隐式神经表示表现出了令人信服的结果3D重建，并且最近也证明了在线大满贯系统的潜力。但是，将它们应用于自主3D重建，在此尚未研究机器人探索场景并计划重建的视图路径的情况下。在本文中，我们首次通过解决两个关键挑战来首次探索自动3D场景重建的可能性：1）寻求标准以根据新表示形式衡量候选人观点的质量，以及2）从可以推广到不同场景的数据而不是手工制作的数据中学习标准。对于第一个挑战，提出了峰值信噪比（PSNR）的代理来量化观点质量。代理是通过将场景中空间点的颜色视为在高斯分布下而不是确定性分布下的随机变量来获得的；分布的方差量化了重建的不确定性并组成代理。在第二个挑战中，代理与场景隐式神经网络的参数共同优化。通过提出的视图质量标准，我们可以将新表示形式应用于自动3D重建。我们的方法证明了与使用TSDF或重建的变体相比，在没有视图计划的情况下，与使用TSDF或重建的变体相比，对各种指标的各种指标进行了重大改进。

Tiktok是一个受欢迎的新社交媒体，用户通过短视频剪辑表达自己。平台上的常见互动形式参与了“挑战”，这是用户迭代的歌曲和舞蹈。挑战传染可以通过复制范围来衡量，即用户上传他们参与挑战的视频。 Tiktok平台的唯一性，其中挑战内容和用户偏好都在不断发展，需要挑战和用户表示的组合。本文通过预测用户的参与调查Tiktok挑战的社会传染。我们提出了一种新的深度学习模型，深度学习模型，学习和组合潜在的用户和挑战表格，以执行此用户挑战预测任务。我们从Fortoupage，App的登陆页面上的12个趋势挑战收集超过7,000个视频的数据集，从1303名用户提供超过10,000个视频。进行了广泛的实验，结果表明，我们所提出的Deepballenger（F1 = 0.494）在预测任务中优于基线（F1 = 0.188）。

数据增强是自然语言处理（NLP）模型的鲁棒性评估的重要组成部分，以及增强他们培训的数据的多样性。在本文中，我们呈现NL-Cogmenter，这是一种新的参与式Python的自然语言增强框架，它支持创建两个转换（对数据的修改）和过滤器（根据特定功能的数据拆分）。我们描述了框架和初始的117个变换和23个过滤器，用于各种自然语言任务。我们通过使用其几个转换来分析流行自然语言模型的鲁棒性来证明NL-Upmenter的功效。基础架构，Datacards和稳健性分析结果在NL-Augmenter存储库上公开可用（\ url {https://github.com/gem-benchmark/nl-augmenter}）。

AI正在经历范式转变，随着模型的兴起（例如Bert，Dall-E，GPT-3），这些模型经过大规模的数据训练，并且可以适应广泛的下游任务。我们称这些模型基础模型来强调其至关重要但不完整的特征。该报告提供了基础模型的机会和风险的详尽说明，包括其功能（例如语言，愿景，机器人技术，推理，人类互动）和技术原则（例如，模型架构，培训程序，数据，系统，安全，安全性，评估，理论）对其应用（例如法律，医疗保健，教育）和社会影响（例如不平等，滥用，经济和环境影响，法律和道德考虑）。尽管基础模型基于标准的深度学习和转移学习，但它们的规模导致了新的新兴能力，以及它们在许多任务中的有效性都激发了同质化。同质化提供了强大的杠杆作用，但要求谨慎，因为基础模型的缺陷均由下游的所有适应模型继承。尽管即将广泛地部署基础模型，但我们目前对它们的工作方式，失败以及由于其新兴属性的影响而缺乏清晰的了解。为了解决这些问题，我们认为基础模型的许多批判性研究都需要与他们的基本社会技术性质相称。

We propose the first joint audio-video generation framework that brings engaging watching and listening experiences simultaneously, towards high-quality realistic videos. To generate joint audio-video pairs, we propose a novel Multi-Modal Diffusion model (i.e., MM-Diffusion), with two-coupled denoising autoencoders. In contrast to existing single-modal diffusion models, MM-Diffusion consists of a sequential multi-modal U-Net for a joint denoising process by design. Two subnets for audio and video learn to gradually generate aligned audio-video pairs from Gaussian noises. To ensure semantic consistency across modalities, we propose a novel random-shift based attention block bridging over the two subnets, which enables efficient cross-modal alignment, and thus reinforces the audio-video fidelity for each other. Extensive experiments show superior results in unconditional audio-video generation, and zero-shot conditional tasks (e.g., video-to-audio). In particular, we achieve the best FVD and FAD on Landscape and AIST++ dancing datasets. Turing tests of 10k votes further demonstrate dominant preferences for our model. The code and pre-trained models can be downloaded at https://github.com/researchmm/MM-Diffusion.