Answering complex logical queries on incomplete knowledge graphs is a challenging task, and has been widely studied. Embedding-based methods require training on complex queries, and cannot generalize well to out-of-distribution query structures. Recent work frames this task as an end-to-end optimization problem, and it only requires a pretrained link predictor. However, due to the exponentially large combinatorial search space, the optimal solution can only be approximated, limiting the final accuracy. In this work, we propose QTO (Query Tree Optimization) that can efficiently find the exact optimal solution. QTO finds the optimal solution by a forward-backward propagation on the tree-like computation graph, i.e., query tree. In particular, QTO utilizes the independence encoded in the query tree to reduce the search space, where only local computations are involved during the optimization procedure. Experiments on 3 datasets show that QTO obtains state-of-the-art performance on complex query answering, outperforming previous best results by an average of 22%. Moreover, QTO can interpret the intermediate solutions for each of the one-hop atoms in the query with over 90% accuracy.

Pre-trained Language Models (PLMs) which are trained on large text corpus through the self-supervised learning method, have yielded promising performance on various tasks in Natural Language Processing (NLP). However, though PLMs with huge parameters can effectively possess rich knowledge learned from massive training text and benefit downstream tasks at the fine-tuning stage, they still have some limitations such as poor reasoning ability due to the lack of external knowledge. Incorporating knowledge into PLMs has been tried to tackle these issues. In this paper, we present a comprehensive review of Knowledge-Enhanced Pre-trained Language Models (KE-PLMs) to provide a clear insight into this thriving field. We introduce appropriate taxonomies respectively for Natural Language Understanding (NLU) and Natural Language Generation (NLG) to highlight the focus of these two kinds of tasks. For NLU, we take several types of knowledge into account and divide them into four categories: linguistic knowledge, text knowledge, knowledge graph (KG), and rule knowledge. The KE-PLMs for NLG are categorized into KG-based and retrieval-based methods. Finally, we point out some promising future directions of KE-PLMs.

Conceptual knowledge is fundamental to human cognition and knowledge bases. However, existing knowledge probing works only focus on evaluating factual knowledge of pre-trained language models (PLMs) and ignore conceptual knowledge. Since conceptual knowledge often appears as implicit commonsense behind texts, designing probes for conceptual knowledge is hard. Inspired by knowledge representation schemata, we comprehensively evaluate conceptual knowledge of PLMs by designing three tasks to probe whether PLMs organize entities by conceptual similarities, learn conceptual properties, and conceptualize entities in contexts, respectively. For the tasks, we collect and annotate 24k data instances covering 393 concepts, which is COPEN, a COnceptual knowledge Probing bENchmark. Extensive experiments on different sizes and types of PLMs show that existing PLMs systematically lack conceptual knowledge and suffer from various spurious correlations. We believe this is a critical bottleneck for realizing human-like cognition in PLMs. COPEN and our codes are publicly released at https://github.com/THU-KEG/COPEN.

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.

The recent prevalence of pretrained language models (PLMs) has dramatically shifted the paradigm of semantic parsing, where the mapping from natural language utterances to structured logical forms is now formulated as a Seq2Seq task. Despite the promising performance, previous PLM-based approaches often suffer from hallucination problems due to their negligence of the structural information contained in the sentence, which essentially constitutes the key semantics of the logical forms. Furthermore, most works treat PLM as a black box in which the generation process of the target logical form is hidden beneath the decoder modules, which greatly hinders the model's intrinsic interpretability. To address these two issues, we propose to incorporate the current PLMs with a hierarchical decoder network. By taking the first-principle structures as the semantic anchors, we propose two novel intermediate supervision tasks, namely Semantic Anchor Extraction and Semantic Anchor Alignment, for training the hierarchical decoders and probing the model intermediate representations in a self-adaptive manner alongside the fine-tuning process. We conduct intensive experiments on several semantic parsing benchmarks and demonstrate that our approach can consistently outperform the baselines. More importantly, by analyzing the intermediate representations of the hierarchical decoders, our approach also makes a huge step toward the intrinsic interpretability of PLMs in the domain of semantic parsing.

视频时间基础（VTG）的目标是根据自然语言（NL）描述在未修剪视频中定位时间矩。由于现实世界的应用程序提供了永无止境的视频流，因此它提出了对长形视频的时间基础的需求，这导致了两个主要挑战：（1）长视频长度使得很难处理整个视频而不减少样本速率并导致高计算负担； （2）随着候选时间的增加数量，准确的多模式对准更具挑战性。为了应对这些挑战，我们提出了一个有效的以窗户为中心的粗略对齐框架，它可以灵活地处理具有较高推理速度的长格式视频输入，并通过我们的新颖的Choce-Fine Muly-Fine增强了时间基础模态对齐框架。具体来说，我们通过滑动窗口方法将长视频将长视频切成候选窗口。 Cone（1）以窗户为中心，通过对比度学习和通过对NL查询相关的候选窗口进行过滤来学习窗口间的（粗粒）语义差异，并且（2）执行内部（罚款） - 使用强大的对比视力文本预训练模型的强大多模式对齐能力对候选力矩进行排名。长期视频的两个大规模VTG基准测试的广泛实验始终显示出可观的性能增长（MAD的3.13％至6.87％，从10.46％到EGO4D-NLQ上的10.46％至13.46％），并且Cone在两个数据集上都可以达到SOTA结果。分析揭示了组件的有效性和长期视频接地的效率较高，因为我们的系统在EGO4D-NLQ上提高了2倍的推理速度，而在MAD上提高了15倍的速度，同时保持了锥体的SOTA性能。

任意形状的文本检测是一项具有挑战性的任务，这是由于大小和宽高比，任意取向或形状，不准确的注释等各种变化的任务。最近引起了大量关注。但是，文本的准确像素级注释是强大的，现有的场景文本检测数据集仅提供粗粒的边界注释。因此，始终存在大量错误分类的文本像素或背景像素，从而降低基于分割的文本检测方法的性能。一般来说，像素是否属于文本与与相邻注释边界的距离高度相关。通过此观察，在本文中，我们通过概率图提出了一种创新且可靠的基于分割的检测方法，以准确检测文本实例。为了具体，我们采用Sigmoid alpha函数（SAF）将边界及其内部像素之间的距离传输到概率图。但是，由于粗粒度文本边界注释的不确定性，一个概率图无法很好地覆盖复杂的概率分布。因此，我们采用一组由一系列Sigmoid alpha函数计算出的概率图来描述可能的概率分布。此外，我们提出了一个迭代模型，以学习预测和吸收概率图，以提供足够的信息来重建文本实例。最后，采用简单的区域生长算法来汇总概率图以完成文本实例。实验结果表明，我们的方法在几个基准的检测准确性方面实现了最先进的性能。

基于图像的3D检测是自主驾驶感知系统的必不可少的组成部分。但是，它仍然受到不满意的表现，这是有限的培训数据的主要原因之一。不幸的是，在3D空间中注释对象是极度时间/资源消耗的，这使得很难任意扩展训练集。在这项工作中，我们专注于半监督的方式，并探索更便宜的替代方案（即伪标记）的可行性，以利用未标记的数据。为此，我们进行了广泛的实验，以研究伪标签是否可以在不同环境下为基线模型提供有效的监督。实验结果不仅证明了基于图像的3D检测的伪标记机制的有效性（例如，在单眼设置下，我们在没有铃铛和哨声的Kitti-3D测试集上实现了20.23 AP，用于中等水平，从6.03 AP），但还显示了几个有趣且值得注意的发现（例如，经过伪标签训练的模型的性能要比基于相同培训数据的地面真相注释训练的表现更好）。我们希望这项工作可以在半监督环境下为基于图像的3D检测社区提供见解。代码，伪标签和预培训模型将公开可用。

Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and output syntax. Recent works have proposed several forms of supplementary supervision but none is generalized across multiple formal languages. This paper proposes a unified intermediate representation (IR) for graph query languages, named GraphQ IR. It has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure. Therefore, a neural semantic parser can more precisely convert user queries into GraphQ IR, which can be later losslessly compiled into various downstream graph query languages. Extensive experiments on several benchmarks including KQA Pro, Overnight, GrailQA, and MetaQA-Cypher under standard i.i.d., out-of-distribution, and low-resource settings validate GraphQ IR's superiority over the previous state-of-the-arts with a maximum 11% accuracy improvement.

为了增强对多模式知识库和多模式信息处理的研究，我们提出了一项具有多模式知识库（MKB）的称为多模式实体标签（MET）的新任务。我们还使用现有的MKB开发了问题的数据集。在MKB中，有实体及其相关的文本和图像。在MET中，给定文本图像对，一个人使用MKB中的信息自动识别文本图像对中的相关实体。我们通过使用信息检索范式来解决任务，并使用NLP和CV中的最新方法实现多个基线。我们进行了广泛的实验，并对实验结果进行了分析。结果表明，任务具有挑战性，但是当前的技术可以实现相对较高的性能。我们将发布数据集，代码和模型，以供将来研究。