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.

在本文中，我们提出了Tetris，这是一个面向目标脚本完成的新任务。与以前的工作不同，它考虑了一个更现实，更通用的设置，其中输入不仅包括目标，还包括其他用户上下文，包括偏好和历史记录。为了使用基于知识的方法解决问题，我们介绍了任务概念图，这是一种自动从教学网站构建的知识库。不同于常识知识基础（例如ConceptNet），任务概念图架构架构介绍了专门用于完成任务的各种基于名词短语的节点。为了将这些图形集成到脚本学习中，我们设计了两种从知识库中获取概念的方法，以作为下游脚本完成的提示。在我们的基于Wikihow的数据集中，我们发现从任务概念图中合并概念会始终提高性能，并证明任务概念图的好处。此外，具有金色标准概念的模型迅速胜过基线，进一步证实了在目标脚本完成中对特定于任务知识的需求。数据集，存储库，模型和演示将公开使用，以促进对这项新任务的进一步研究。

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。

斑马鱼是一种出色的模型生物，已在生物实验，药物筛查和群智能领域广泛使用。近年来，有许多用于跟踪行为研究涉及斑马鱼的技术，这使其攻击许多领域的科学家的注意力。斑马鱼的多目标跟踪仍然面临许多挑战。高流动性和不确定性使得难以预测其运动；相似的外观和纹理功能使建立外观模型变得困难。由于频繁的阻塞，甚至很难将轨迹连接起来。在本文中，我们使用粒子过滤器来近似运动的不确定性。首先，通过分析斑马鱼的运动特性，我们建立了一个有效的混合运动模型来预测其位置。然后，我们根据预测位置建立一个外观模型，以预测每个目标的姿势，同时通过比较预测的姿势和观察姿势的差来称量颗粒；最后，我们通过加权位置获得了单斑马鱼的最佳位置，并使用关节颗粒过滤器来处理多个斑马鱼的轨迹链接。

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。

We revisit the one-shot Neural Architecture Search (NAS) paradigm and analyze its advantages over existing NAS approaches. Existing one-shot method, however, is hard to train and not yet effective on large scale datasets like ImageNet. This work propose a Single Path One-Shot model to address the challenge in the training. Our central idea is to construct a simplified supernet, where all architectures are single paths so that weight co-adaption problem is alleviated. Training is performed by uniform path sampling. All architectures (and their weights) are trained fully and equally. Comprehensive experiments verify that our approach is flexible and effective. It is easy to train and fast to search. It effortlessly supports complex search spaces (e.g., building blocks, channel, mixed-precision quantization) and different search constraints (e.g., FLOPs, latency). It is thus convenient to use for various needs. It achieves start-of-the-art performance on the large dataset ImageNet.Equal contribution. This work is done when Haoyuan Mu and Zechun Liu are interns at MEGVII Technology.

Domain adaptive detection aims to improve the generalization of detectors on target domain. To reduce discrepancy in feature distributions between two domains, recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning. However, they neglect the relationship between multiple granularities and different features in alignment, degrading detection. Addressing this, we introduce a unified multi-granularity alignment (MGA)-based detection framework for domain-invariant feature learning. The key is to encode the dependencies across different granularities including pixel-, instance-, and category-levels simultaneously to align two domains. Specifically, based on pixel-level features, we first develop an omni-scale gated fusion (OSGF) module to aggregate discriminative representations of instances with scale-aware convolutions, leading to robust multi-scale detection. Besides, we introduce multi-granularity discriminators to identify where, either source or target domains, different granularities of samples come from. Note that, MGA not only leverages instance discriminability in different categories but also exploits category consistency between two domains for detection. Furthermore, we present an adaptive exponential moving average (AEMA) strategy that explores model assessments for model update to improve pseudo labels and alleviate local misalignment problem, boosting detection robustness. Extensive experiments on multiple domain adaption scenarios validate the superiority of MGA over other approaches on FCOS and Faster R-CNN detectors. Code will be released at https://github.com/tiankongzhang/MGA.

Zero-Shot Learning has been a highlighted research topic in both vision and language areas. Recently, most existing methods adopt structured knowledge information to model explicit correlations among categories and use deep graph convolutional network to propagate information between different categories. However, it is difficult to add new categories to existing structured knowledge graph, and deep graph convolutional network suffers from over-smoothing problem. In this paper, we provide a new semantic enhanced knowledge graph that contains both expert knowledge and categories semantic correlation. Our semantic enhanced knowledge graph can further enhance the correlations among categories and make it easy to absorb new categories. To propagate information on the knowledge graph, we propose a novel Residual Graph Convolutional Network (ResGCN), which can effectively alleviate the problem of over-smoothing. Experiments conducted on the widely used large-scale ImageNet-21K dataset and AWA2 dataset show the effectiveness of our method, and establish a new state-of-the-art on zero-shot learning. Moreover, our results on the large-scale ImageNet-21K with various feature extraction networks show that our method has better generalization and robustness.

In subcellular biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures. However, fluorescence staining is slow, expensive, and harmful to cells. In this paper, we treat it as a deep learning task termed subcellular structure prediction (SSP), aiming to predict the 3D fluorescent images of multiple subcellular structures from a 3D transmitted-light image. Unfortunately, due to the limitations of current biotechnology, each image is partially labeled in SSP. Besides, naturally, the subcellular structures vary considerably in size, which causes the multi-scale issue in SSP. However, traditional solutions can not address SSP well since they organize network parameters inefficiently and inflexibly. To overcome these challenges, we propose Re-parameterizing Mixture-of-Diverse-Experts (RepMode), a network that dynamically organizes its parameters with task-aware priors to handle specified single-label prediction tasks of SSP. In RepMode, the Mixture-of-Diverse-Experts (MoDE) block is designed to learn the generalized parameters for all tasks, and gating re-parameterization (GatRep) is performed to generate the specialized parameters for each task, by which RepMode can maintain a compact practical topology exactly like a plain network, and meanwhile achieves a powerful theoretical topology. Comprehensive experiments show that RepMode outperforms existing methods on ten of twelve prediction tasks of SSP and achieves state-of-the-art overall performance.

Previous work on controllable text generation has explored the idea of control from the latent space, such as optimizing a representation with attribute-related classifiers or sampling a representation from relevant discrete samples. However, they are not effective enough in modeling both the latent space and the control, leaving controlled text with low quality and diversity. In this work, we propose a novel control framework using probability density estimation in the latent space. Our method utilizes an invertible transformation function, the Normalizing Flow, that maps the complex distributions in the latent space to simple Gaussian distributions in the prior space. Thus, we can perform sophisticated and flexible control in the prior space and feed the control effects back into the latent space owing to the one-one-mapping property of invertible transformations. Experiments on single-attribute controls and multi-attribute control reveal that our method outperforms several strong baselines on attribute relevance and text quality and achieves the SOTA. Further analysis of control strength adjustment demonstrates the flexibility of our control strategy.