Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt / 30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper.

在过去的十年中，AI AID毒品发现（AIDD）的计算方法和数据集策划的繁荣发展。但是，现实世界中的药物数据集经常表现出高度不平衡的分布，这在很大程度上被当前的文献忽略了，但可能会严重损害机器学习应用程序的公平性和概括。在这一观察结果的激励下，我们介绍了Imdrug，这是一个全面的基准标准，其开源python库由4个不平衡设置，11个AI-Ready数据集，54个学习任务和16种为不平衡学习量身定制的基线算法。它为涵盖广泛的药物发现管道（例如分子建模，药物靶标相互作用和逆合合成）的问题和解决方案提供了可访问且可定制的测试床。我们通过新的评估指标进行广泛的实证研究，以证明现有算法在数据不平衡情况下无法解决药物和药物挑战。我们认为，Imdrug为未来的研究和发展开辟了途径，在AIDD和深度不平衡学习的交集中对现实世界中的挑战开辟了道路。

随着移动设备的普及，例如智能手机和可穿戴设备，更轻，更快的型号对于应用视频超级分辨率至关重要。但是，大多数以前的轻型模型倾向于集中于减少台式GPU模型推断的范围，这在当前的移动设备中可能不会节能。在本文中，我们提出了极端低功率超级分辨率（ELSR）网络，该网络仅在移动设备中消耗少量的能量。采用预训练和填充方法来提高极小模型的性能。广泛的实验表明，我们的方法在恢复质量和功耗之间取得了良好的平衡。最后，我们在目标总经理Dimenty 9000 PlantForm上，PSNR 27.34 dB和功率为0.09 w/30fps的竞争分数为90.9，在移动AI＆AIM 2022实时视频超级分辨率挑战中排名第一。

在本文中，我们提出了一种识别相同商品的方法。在电子商务方案中，通常通过图像和文本来描述商品。根据定义，相同的商品是具有相同关键属性并且认知与消费者相同的商品。有两个主要挑战：1）多模式表示的提取和融合。 2）通过比较阈值的表示之间的距离来验证两种商品是否相同的能力。为了解决上述问题，我们提出了一种基于自适应阈值的端到端相同的商品验证方法。我们使用双流网络分别提取商品嵌入和阈值嵌入，然后将它们串联以获得商品表示。我们的方法能够根据不同的商品获得不同的阈值，同时保持整个商品表示的索引性。我们在实验中验证了多模式特征融合的有效性和自适应阈值的优势。此外，我们的方法达到了0.8936的F1分数，并在排行榜上排名第三，完成了CCKS-2022知识图评估数字商务竞赛的第二项任务。代码和预估计的模型可在https://github.com/hanchenchen/ccks2022-track2-solution上找到。

现实世界的行为通常是由多种代理之间复杂的相互作用来塑造的。为了可靠地研究多代理行为，无监督和自我监督的学习的进步使从轨迹数据中学到了各种不同的行为表示。迄今为止，还没有一组统一的基准测试，可以在广泛的行为分析设置中进行定量和系统地比较方法。我们的目的是通过引入来自现实世界行为神经科学实验的大规模，多代理轨迹数据集来解决这一问题，该数据集涵盖了一系列行为分析任务。我们的数据集由来自通用模型生物的轨迹数据组成，其中有960万帧的小鼠数据和440万帧的飞行数据，在各种实验环境中，例如不同的菌株，相互作用的长度和光遗传学刺激。框架的子集还包括专家注销的行为标签。我们数据集的改进对应于跨多种生物的行为表示，并能够捕获常见行为分析任务的差异。

节点分类和图形分类是两个图形学习问题，分别预测节点的类标签和图形的类标签。图的节点通常代表现实世界实体，例如，社交网络中的用户或文档引用网络中的文档。在这项工作中，我们考虑了一个更具挑战性但实际上有用的设置，其中节点本身是图形实例。这导致了层次图的观点，该视角在许多领域（例如社交网络，生物网络和文档收集）中产生。我们在层次图中研究节点分类问题，其中“节点”是图形实例。由于标签通常受到限制，我们设计了一种新型的半监督溶液，名为Seal-CI。 Seal-CI采用了一个迭代框架，该框架需要轮流更新两个模块，一个模块在图形实例级别，另一个在层次图级别上进行。为了在不同级别的层次图之间执行一致性，我们提出了分层图共同信息（HGMI），并进一步提出了一种使用理论保证计算HGMI的方法。我们证明了该层次图建模的有效性以及在文本和社交网络数据上提出的密封CI方法。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.

This paper illustrates the technologies of user next intent prediction with a concept knowledge graph. The system has been deployed on the Web at Alipay, serving more than 100 million daily active users. Specifically, we propose AlipayKG to explicitly characterize user intent, which is an offline concept knowledge graph in the Life-Service domain modeling the historical behaviors of users, the rich content interacted by users and the relations between them. We further introduce a Transformer-based model which integrates expert rules from the knowledge graph to infer the online user's next intent. Experimental results demonstrate that the proposed system can effectively enhance the performance of the downstream tasks while retaining explainability.