Recently, there has been increasing interest in synthesizing data to improve downstream text-to-SQL tasks. In this paper, we first examined the existing synthesized datasets and discovered that state-of-the-art text-to-SQL algorithms did not further improve on popular benchmarks when trained with augmented synthetic data. We observed two shortcomings: illogical synthetic SQL queries from independent column sampling and arbitrary table joins. To address these issues, we propose a novel synthesis framework that incorporates key relationships from schema, imposes strong typing, and conducts schema-distance-weighted column sampling. We also adopt an intermediate representation (IR) for the SQL-to-text task to further improve the quality of the generated natural language questions. When existing powerful semantic parsers are pre-finetuned on our high-quality synthesized data, our experiments show that these models have significant accuracy boosts on popular benchmarks, including new state-of-the-art performance on Spider.

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.

Speech-to-speech translation directly translates a speech utterance to another between different languages, and has great potential in tasks such as simultaneous interpretation. State-of-art models usually contains an auxiliary module for phoneme sequences prediction, and this requires textual annotation of the training dataset. We propose a direct speech-to-speech translation model which can be trained without any textual annotation or content information. Instead of introducing an auxiliary phoneme prediction task in the model, we propose to use bottleneck features as intermediate training objectives for our model to ensure the translation performance of the system. Experiments on Mandarin-Cantonese speech translation demonstrate the feasibility of the proposed approach and the performance can match a cascaded system with respect of translation and synthesis qualities.

Dataset Distillation (DD), a newly emerging field, aims at generating much smaller and high-quality synthetic datasets from large ones. Existing DD methods based on gradient matching achieve leading performance; however, they are extremely computationally intensive as they require continuously optimizing a dataset among thousands of randomly initialized models. In this paper, we assume that training the synthetic data with diverse models leads to better generalization performance. Thus we propose two \textbf{model augmentation} techniques, ~\ie using \textbf{early-stage models} and \textbf{weight perturbation} to learn an informative synthetic set with significantly reduced training cost. Extensive experiments demonstrate that our method achieves up to 20$\times$ speedup and comparable performance on par with state-of-the-art baseline methods.

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.

作为多媒体信息检索中越来越流行的任务，视频瞬间检索（VMR）旨在根据给定的语言查询从未修剪视频中定位目标时刻。以前的大多数方法都在很大程度上取决于众多手动注释（即瞬间边界），在实践中获取非常昂贵。此外，由于不同数据集之间的域间隙，直接将这些预训练的模型应用于看不见的域，这会导致显着的性能下降。在本文中，我们专注于一项新任务：跨域VMR，其中一个域中完全注重数据集（````源域'''），但是感兴趣的域（``目标域''）仅包含未通知的数据集。据我们所知，我们介绍了有关跨域VMR的第一项研究。为了解决这一新任务，我们提出了一个新型的多模式跨域比对（MMCDA）网络，以将注释知识从源域转移到目标域。但是，由于源和目标域之间的域差异以及视频和查询之间的语义差距，直接将经过训练的模型应用于目标域通常会导致性能下降。为了解决这个问题，我们开发了三个新型模块：（i）域对齐模块旨在使每种模式的不同域之间的特征分布对齐； （ii）跨模式对齐模块旨在将视频和查询特征映射到关节嵌入空间中，并将目标域不同模态之间的特征分布对齐； （iii）特定的比对模块试图获得特定帧与给定查询之间的细粒度相似性以进行最佳定位。通过共同训练这三个模块，我们的MMCDA可以学习域不变和语义一致的跨模式表示。

近年来，在自学学习（SSL）方面取得了重大成功，这有助于各种下游任务。但是，攻击者可能会窃取此类SSL模型并将其商业化以获利，这对于保护其知识产权（IP）至关重要。大多数现有的IP保护解决方案都是为监督学习模型而设计的，不能直接使用，因为它们要求模型的下游任务和目标标签在水印嵌入过程中已知并获得，这在SSL的域中并非总是可以的。为了解决此类问题，尤其是在水印嵌入过程中下游任务多样化且未知时，我们提出了一种新型的黑盒水印解决方案，名为SSL-WM，以保护SSL模型的所有权。 SSL-WM将水印编码器的水印输入映射到不变的表示空间中，该空间会导致任何下游分类器产生预期的行为，从而允许检测到嵌入式水印。我们使用不同的SSL模型（包括基于对比度和基于生成的生成型）来评估许多任务，例如计算机视觉（CV）和自然语言处理（NLP）等许多任务。实验结果表明，SSL-WM可以有效地验证各种下游任务中被盗SSL模型的所有权。此外，SSL-WM对模型进行微调和修剪攻击非常强大。最后，SSL-WM还可以从评估的水印检测方法中逃避检测，从而证明了其在保护SSL模型IP时的有希望的应用。

本文研究了时间句子接地的多媒体问题（TSG），该问题旨在根据给定的句子查询准确地确定未修剪视频中的特定视频段。传统的TSG方法主要遵循自上而下或自下而上的框架，不是端到端。他们严重依靠耗时的后处理来完善接地结果。最近，提出了一些基于变压器的方法来有效地对视频和查询之间的细粒语义对齐进行建模。尽管这些方法在一定程度上达到了显着的性能，但它们同样将视频的框架和查询的单词视为用于关联的变压器输入，未能捕获其不同水平的粒度与独特的语义。为了解决这个问题，在本文中，我们提出了一种新型的等级局部 - 全球变压器（HLGT）来利用这种层次结构信息，并模拟不同粒度的不同级别的相互作用和不同的模态之间的相互作用，以学习更多细粒度的多模式表示。具体而言，我们首先将视频和查询分为单个剪辑和短语，以通过时间变压器学习其本地上下文（相邻依赖关系）和全局相关性（远程依赖）。然后，引入了全球本地变压器，以了解本地级别和全球级别语义之间的相互作用，以提供更好的多模式推理。此外，我们开发了一种新的跨模式周期一致性损失，以在两种模式之间实施相互作用，并鼓励它们之间的语义一致性。最后，我们设计了一个全新的跨模式平行变压器解码器，以集成编码的视觉和文本特征，以进行最终接地。在三个具有挑战性的数据集上进行了广泛的实验表明，我们提出的HLGT实现了新的最新性能。

视觉模型最近在许多计算机视觉任务上显示出巨大的潜力。同时，与线性探针相比，先前的工作表明，与线性探针相比，这是较少的图像识别的迅速调整，可以在很少的图像识别上获得卓越的性能。在实际应用程序中，相关的几个射击任务是相关的，尤其是在专业领域。但是，以前的工作忽略了此类信息。受到以下事实的启发，即通过多任务学习通常可以提高性能，我们提出了一种新颖的方法softcpt（迅速调整的软上下文共享），以微调多个目标几个目标任务的预训练的视觉模型， 同时。具体来说，我们设计了一个任务共享的元网络，以使用预定义的任务名称以及可学习的元提示为输入为每个任务生成提示向量。因此，所有任务的迅速向量将以软的方式共享。该共享的元网络的参数以及元提示向量都在所有目标任务的联合培训集中调整。在三个多任务少量数据集上进行的广泛实验表明，SoftCpt的表现优于代表性的单任务提示方法Coop [78]，这意味着多任务学习在视觉及时及时调整中的有效性。源代码和数据将公开可用。

在计算机视觉中，微调是利用预训练的视觉模型来执行下游任务的事实上的方法。但是，由于采用参数效率低下的全局更新并严重依赖于高质量的下游数据，因此在实践中部署它是非常具有挑战性的。最近，基于及时的学习添加了与任务相关的提示，以使下游任务适应预训练的模型，从而极大地提高了许多自然语言下游任务的性能。在这项工作中，我们扩展了这种显着的转移能力，从迅速的愿景模型中受益，以替代微调。为此，我们提出了参数有效的及时调整（亲调整），以使冷冻视觉模型适应各种下游视觉任务。实行调整的关键是基于及时的调整，即学习特定于任务的视觉提示，以使用预先训练的模型冷冻的下游输入图像。通过仅培训一些其他参数，它可以在基于CNN和基于变压器的各种架构上工作。广泛的实验证据表明，在广泛的视觉任务和场景中，主张表现优于微调，包括图像分类（通用对象，类失衡，图像腐败，对抗性稳定性和分布范围内的概括）和密集的预测任务例如对象检测和语义分割。