Structured tabular data exist across nearly all fields. Reasoning task over these data aims to answer questions or determine the truthiness of hypothesis sentences by understanding the semantic meaning of a table. While previous works have devoted significant efforts to the tabular reasoning task, they always assume there are sufficient labeled data. However, constructing reasoning samples over tables (and related text) is labor-intensive, especially when the reasoning process is complex. When labeled data is insufficient, the performance of models will suffer an unendurable decline. In this paper, we propose a unified framework for unsupervised complex tabular reasoning (UCTR), which generates sufficient and diverse synthetic data with complex logic for tabular reasoning tasks, assuming no human-annotated data at all. We first utilize a random sampling strategy to collect diverse programs of different types and execute them on tables based on a "Program-Executor" module. To bridge the gap between the programs and natural language sentences, we design a powerful "NL-Generator" module to generate natural language sentences with complex logic from these programs. Since a table often occurs with its surrounding texts, we further propose novel "Table-to-Text" and "Text-to-Table" operators to handle joint table-text reasoning scenarios. This way, we can adequately exploit the unlabeled table resources to obtain a well-performed reasoning model under an unsupervised setting. Our experiments cover different tasks (question answering and fact verification) and different domains (general and specific), showing that our unsupervised methods can achieve at most 93% performance compared to supervised models. We also find that it can substantially boost the supervised performance in low-resourced domains as a data augmentation technique. Our code is available at https://github.com/leezythu/UCTR.

Although weakly-supervised techniques can reduce the labeling effort, it is unclear whether a saliency model trained with weakly-supervised data (e.g., point annotation) can achieve the equivalent performance of its fully-supervised version. This paper attempts to answer this unexplored question by proving a hypothesis: there is a point-labeled dataset where saliency models trained on it can achieve equivalent performance when trained on the densely annotated dataset. To prove this conjecture, we proposed a novel yet effective adversarial trajectory-ensemble active learning (ATAL). Our contributions are three-fold: 1) Our proposed adversarial attack triggering uncertainty can conquer the overconfidence of existing active learning methods and accurately locate these uncertain pixels. {2)} Our proposed trajectory-ensemble uncertainty estimation method maintains the advantages of the ensemble networks while significantly reducing the computational cost. {3)} Our proposed relationship-aware diversity sampling algorithm can conquer oversampling while boosting performance. Experimental results show that our ATAL can find such a point-labeled dataset, where a saliency model trained on it obtained $97\%$ -- $99\%$ performance of its fully-supervised version with only ten annotated points per image.

Open Information Extraction (OpenIE) facilitates the open-domain discovery of textual facts. However, the prevailing solutions evaluate OpenIE models on in-domain test sets aside from the training corpus, which certainly violates the initial task principle of domain-independence. In this paper, we propose to advance OpenIE towards a more realistic scenario: generalizing over unseen target domains with different data distributions from the source training domains, termed Generalized OpenIE. For this purpose, we first introduce GLOBE, a large-scale human-annotated multi-domain OpenIE benchmark, to examine the robustness of recent OpenIE models to domain shifts, and the relative performance degradation of up to 70% implies the challenges of generalized OpenIE. Then, we propose DragonIE, which explores a minimalist graph expression of textual fact: directed acyclic graph, to improve the OpenIE generalization. Extensive experiments demonstrate that DragonIE beats the previous methods in both in-domain and out-of-domain settings by as much as 6.0% in F1 score absolutely, but there is still ample room for improvement.

Document-level relation extraction (DocRE) aims to identify semantic labels among entities within a single document. One major challenge of DocRE is to dig decisive details regarding a specific entity pair from long text. However, in many cases, only a fraction of text carries required information, even in the manually labeled supporting evidence. To better capture and exploit instructive information, we propose a novel expLicit syntAx Refinement and Subsentence mOdeliNg based framework (LARSON). By introducing extra syntactic information, LARSON can model subsentences of arbitrary granularity and efficiently screen instructive ones. Moreover, we incorporate refined syntax into text representations which further improves the performance of LARSON. Experimental results on three benchmark datasets (DocRED, CDR, and GDA) demonstrate that LARSON significantly outperforms existing methods.

Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper.

识别密集混乱中的物体准确地对各种机器人操纵任务发挥了重要作用，包括抓握，包装，重新安排等。但是，传统的视觉识别模型通常会因为实例之间的严重阻塞而错过对象，并且由于视觉上的歧义与高对象拥挤的歧义导致了不正确的预测。在本文中，我们提出了一个称为Smart Explorer的交互式探索框架，用于识别密集的杂物中的所有对象。我们的Smart Explorer会与混乱物进行物理互动，以最大程度地提高识别性能，同时最大程度地减少动作数量，在这种情况下，可以通过最佳的准确性效率折衷来有效地减轻误报和负面因素。具体而言，我们首先收集混乱的多视图RGB-D图像，然后重建相应的点云。通过跨视图汇总RGB图像的实例分割，我们获得了杂物的实例云分区，该杂物通过该杂物的存在和每个类的对象数量。生成有效物理互动的推动动作可大大减少由实例分割熵和多视图对象分歧组成的识别不确定性。因此，通过迭代实例预测和物理互动实现了对象识别在密集混乱中的最佳精度效率折衷。广泛的实验表明，我们的Smart Explorer仅使用几个动作获得了有希望的识别精度，这也超过了随机推动的大幅度。

主流对象检测器通常由两个子任务组成，包括由两个并行头部实现的分类和回归任务。这种经典的设计范式不可避免地会导致分类得分和本地化质量（IOU）之间的空间分布不一致。因此，本文从知识蒸馏的角度来减轻这种错位。首先，我们观察到，与轻量级学生相比，庞大的老师获得的和谐预测比例更高。基于这个有趣的观察，设计了一种新颖的和谐评分（HS），以估计分类和回归质量的一致性。 HS对两个子任务之间的关系进行建模，并被视为先验知识，以促进学生的和谐预测。其次，这种空间未对准将在提炼特征时会导致选择性区域的选择。为了减轻这个问题，通过灵活平衡分类和回归任务的贡献，提出了一种新颖的任务功能蒸馏（TFD）。最终，HD和TFD构成了所提出的方法，称为任务均衡蒸馏（TBD）。广泛的实验证明了该方法的巨大潜力和概括。具体而言，当配备TBD时，带有Resnet-50的视网膜在可可基准下获得41.0地图，表现优于最近的FGD和FRS。

实体链接旨在将模棱两可的提及与知识库中的相应实体联系起来，这对于各种下游应用程序是重要的，例如知识库完成，问题答案和信息提取。尽管已经致力于这项任务，但这些研究中的大多数遵循以下假设，即可以使用大规模标记的数据。但是，当由于劳动密集型注释工作而导致的标记数据不足以针对特定领域时，现有算法的性能将遭受无法忍受的下降。在本文中，我们努力解决了几个弹药实体链接的问题，这只需要最少的标记数据，并且在实际情况下更为实用。具体而言，我们首先提出了一种新颖的弱监督策略，以基于提及的重写生成非平凡的合成实体对。由于合成数据的质量对有效的模型训练有关键的影响，因此我们进一步设计了一种元学习机制，以自动为每个合成实体对分配不同的权重。通过这种方式，我们可以深刻利用丰富而宝贵的语义信息，从而在几个射击设置下得出训练有素的实体链接模型。现实世界数据集上的实验表明，所提出的方法可以广泛改善最新的几杆实体链接模型，并在只有少量标记的数据可用时实现令人印象深刻的性能。此外，我们还展示了模型可传递性的出色能力。

蛋白质是人类生命的重要组成部分，其结构对于功能和机制分析很重要。最近的工作表明了AI驱动方法对蛋白质结构预测的潜力。但是，新模型的开发受到数据集和基准测试培训程序的限制。据我们所知，现有的开源数据集远不足以满足现代蛋白质序列相关研究的需求。为了解决这个问题，我们介绍了具有高覆盖率和多样性的第一个百万级蛋白质结构预测数据集，称为PSP。该数据集由570K真实结构序列（10TB）和745K互补蒸馏序列（15TB）组成。此外，我们还提供了该数据集上SOTA蛋白结构预测模型的基准测试训练程序。我们通过参与客串比赛验证该数据集的实用程序进行培训，我们的模特赢得了第一名。我们希望我们的PSP数据集以及培训基准能够为AI驱动的蛋白质相关研究提供更广泛的AI/生物学研究人员社区。

6-DOF GRASP姿势检测多盖和多对象是智能机器人领域的挑战任务。为了模仿人类的推理能力来抓住对象，广泛研究了数据驱动的方法。随着大规模数据集的引入，我们发现单个物理度量通常会产生几个离散水平的掌握置信分数，这无法很好地区分数百万的掌握姿势并导致不准确的预测结果。在本文中，我们提出了一个混合物理指标来解决此评估不足。首先，我们定义一个新的度量标准是基于力闭合度量的，并通过对象平坦，重力和碰撞的测量来补充。其次，我们利用这种混合物理指标来产生精致的置信度评分。第三，为了有效地学习新的置信度得分，我们设计了一个称为平面重力碰撞抓氏（FGC-Graspnet）的多分辨率网络。 FGC-GRASPNET提出了多个任务的多分辨率特征学习体系结构，并引入了新的关节损失函数，从而增强了GRASP检测的平均精度。网络评估和足够的实际机器人实验证明了我们混合物理指标和FGC-GraspNet的有效性。我们的方法在现实世界中混乱的场景中达到了90.5 \％的成功率。我们的代码可在https://github.com/luyh20/fgc-graspnet上找到。