最近的几项研究指出，现有的视觉问题回答（VQA）模型严重遭受了先前的问题的困扰，这是指捕获问题类型和答案之间的表面统计相关性，而忽略了图像内容。通过创建精致的模型或引入额外的视觉注释，已经致力于加强图像依赖性。但是，这些方法无法充分探索视觉提示如何显式影响学习的答案表示，这对于减轻语言的依赖至关重要。此外，他们通常强调对学习的答案表示形式的班级歧视，这忽略了更精细的实例级别模式，并要求进一步优化。在本文中，我们从视觉扰动校准的角度提出了一种新颖的协作学习方案，该方案可以更好地研究细粒度的视觉效果，并通过学习实例级别的特征来减轻语言的先验问题。具体而言，我们设计了一个视觉控制器来构建具有不同扰动范围的两种策划图像，基于该图像的协作学习内置不变性和实体歧视的协作学习由两个精心设计的歧视者实现。此外，我们在潜在空间上实施信息瓶颈调制器，以进一步减轻偏见和表示校准。我们将视觉扰动感知框架强加于三个正统基准，并将实验结果对两个诊断性VQA-CP基准数据集进行了实验结果，显然表明了其有效性。此外，我们还证明了它在平衡的VQA基准上的鲁棒性是合理的。

Vision Transformer（VIT）最近由于其出色的模型功能而引起了计算机视觉的极大关注。但是，大多数流行的VIT模型都有大量参数，从而限制了其在资源有限的设备上的适用性。为了减轻这个问题，我们提出了Tinyvit，这是一个新的小型，有效的小型视觉变压器，并通过我们提议的快速蒸馏框架在大型数据集上预处理。核心思想是将知识从大型模型转移到小型模型，同时使小型模型能够获得大量预处理数据的股息。更具体地说，我们在预训练期间应用蒸馏进行知识转移。大型教师模型的徽标被稀疏并提前存储在磁盘中，以节省内存成本和计算开销。微小的学生变形金刚自动从具有计算和参数约束的大型审计模型中缩小。全面的实验证明了TinyVit的功效。它仅具有21m参数的Imagenet-1k上的前1个精度为84.8％，与在Imagenet-21K上预读的SWIN-B相当，而使用较少的参数则使用了4.2倍。此外，增加图像分辨率，TinyVit可以达到86.5％的精度，仅使用11％参数，比SWIN-L略好。最后但并非最不重要的一点是，我们在各种下游任务上展示了TinyVit的良好转移能力。代码和型号可在https://github.com/microsoft/cream/tree/main/tinyvit上找到。

视觉问题回答（VQA）本质上是从根本上组成的，许多问题仅通过将它们分解为模块化子问题就可以回答。最新提出的神经模块网络（NMN）采用此策略来问答案，而在现成的布局解析器或有关网络体系结构设计的其他专家政策中，而不是从数据中学习。这些策略导致对输入的语义复杂差异的适应性不令人满意，从而阻碍了模型的表示能力和概括性。为了解决这个问题，我们提出了一个语义吸引的模块化胶囊路由框架，称为Super，以更好地捕获特定实例的视觉 - 语义特征并完善预测的判别性表示。特别是，在超级网络的每一层中都定制了五个功能强大的专用模块以及动态路由器，并构造了紧凑的路由空间，使得可以充分利用各种可自定义的路由，并且可以明确校准视觉声称表示。我们相对证明，我们提出的超级方案在五个基准数据集以及参数效率优势上的有效性和概括能力合理。值得强调的是，这项工作不是在VQA中追求最先进的结果。取而代之的是，我们希望我们的模型有责任为VQA提供建筑学习和表示校准的新颖观点。

用于对象检测的常规知识蒸馏（KD）方法主要集中于同质的教师学生探测器。但是，用于部署的轻质检测器的设计通常与高容量探测器显着不同。因此，我们研究了异构教师对之间的KD，以进行广泛的应用。我们观察到，异质KD（异核KD）的核心难度是由于不同优化的方式而导致异质探测器的主链特征之间的显着语义差距。常规的同质KD（HOMO-KD）方法遭受了这种差距的影响，并且很难直接获得异性KD的令人满意的性能。在本文中，我们提出了异助剂蒸馏（Head）框架，利用异质检测头作为助手来指导学生探测器的优化以减少此间隙。在头上，助手是一个额外的探测头，其建筑与学生骨干的老师负责人同质。因此，将异源KD转变为同性恋，从而可以从老师到学生的有效知识转移。此外，当训练有素的教师探测器不可用时，我们将头部扩展到一个无教师的头（TF-Head）框架。与当前检测KD方法相比，我们的方法已取得了显着改善。例如，在MS-COCO数据集上，TF-Head帮助R18视网膜实现33.9 MAP（+2.2），而Head将极限进一步推到36.2 MAP（+4.5）。

视觉变压器在识别和检测等实质性视野任务中显示了很大的视觉表示功率，从而在手动设计更有效的架构方面吸引了快速增长的努力。在本文中，我们建议使用神经架构搜索来自动化此过程，不仅可以搜索架构，还可以搜索搜索空间。中央观点是逐步发展使用权重共享超空网的E-T错误引导的不同搜索维度。此外，我们提供了一般视觉变压器的设计指南，根据空间搜索过程进行广泛的分析，这可以促进对视觉变压器的理解。值得注意的是，搜索空间的搜索模型，名为S3（用于搜索空间的短路），从搜索到的空间实现了卓越的性能，以最近提出的型号，例如在ImageNet上进行评估时的Swin，Deit和Vit。 S3的有效性也在对象检测，语义细分和视觉问题上说明，展示其泛度到下游视觉和视觉语言任务。代码和型号将在https://github.com/microsoft/cream中使用。

段4K或6K超高分辨率图像需要在图像分割中考虑额外的计算考虑。常见的策略，如淡化采样，补丁裁剪和级联模型，不能妥善解决精度和计算成本之间的余额问题。由人类在粗糙到精确水平中连续地区分物体的影响，我们提出了用于超高分辨率分割任务的连续细化模型〜（CRM）。CRM连续将特征映射与细化目标保持一致，并聚合要重建这些图像的细节。此外，我们的CRM表明其具有填补低分辨率培训图像和超高分辨率测试之间的分辨率差距的重要概括能力。我们展示了定量的绩效评估和可视化，以表明我们的提出方法在图像分割细化方面是快速有效的。代码将在https://github.com/dvlab-research/entity发布。

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification the explainability of GNNs has become an indispensable component for predictive and trustworthy decision-making. Thus, it is critical to explain why graph neural network (GNN) makes particular predictions for them to be believed in many applications. Some GNNs explainers have been proposed recently. However, they lack to generate accurate and real explanations. To mitigate these limitations, we propose GANExplainer, based on Generative Adversarial Network (GAN) architecture. GANExplainer is composed of a generator to create explanations and a discriminator to assist with the Generator development. We investigate the explanation accuracy of our models by comparing the performance of GANExplainer with other state-of-the-art methods. Our empirical results on synthetic datasets indicate that GANExplainer improves explanation accuracy by up to 35\% compared to its alternatives.

Video Super-Resolution (VSR) aims to restore high-resolution (HR) videos from low-resolution (LR) videos. Existing VSR techniques usually recover HR frames by extracting pertinent textures from nearby frames with known degradation processes. Despite significant progress, grand challenges are remained to effectively extract and transmit high-quality textures from high-degraded low-quality sequences, such as blur, additive noises, and compression artifacts. In this work, a novel Frequency-Transformer (FTVSR) is proposed for handling low-quality videos that carry out self-attention in a combined space-time-frequency domain. First, video frames are split into patches and each patch is transformed into spectral maps in which each channel represents a frequency band. It permits a fine-grained self-attention on each frequency band, so that real visual texture can be distinguished from artifacts. Second, a novel dual frequency attention (DFA) mechanism is proposed to capture the global frequency relations and local frequency relations, which can handle different complicated degradation processes in real-world scenarios. Third, we explore different self-attention schemes for video processing in the frequency domain and discover that a ``divided attention'' which conducts a joint space-frequency attention before applying temporal-frequency attention, leads to the best video enhancement quality. Extensive experiments on three widely-used VSR datasets show that FTVSR outperforms state-of-the-art methods on different low-quality videos with clear visual margins. Code and pre-trained models are available at https://github.com/researchmm/FTVSR.

We propose the first joint audio-video generation framework that brings engaging watching and listening experiences simultaneously, towards high-quality realistic videos. To generate joint audio-video pairs, we propose a novel Multi-Modal Diffusion model (i.e., MM-Diffusion), with two-coupled denoising autoencoders. In contrast to existing single-modal diffusion models, MM-Diffusion consists of a sequential multi-modal U-Net for a joint denoising process by design. Two subnets for audio and video learn to gradually generate aligned audio-video pairs from Gaussian noises. To ensure semantic consistency across modalities, we propose a novel random-shift based attention block bridging over the two subnets, which enables efficient cross-modal alignment, and thus reinforces the audio-video fidelity for each other. Extensive experiments show superior results in unconditional audio-video generation, and zero-shot conditional tasks (e.g., video-to-audio). In particular, we achieve the best FVD and FAD on Landscape and AIST++ dancing datasets. Turing tests of 10k votes further demonstrate dominant preferences for our model. The code and pre-trained models can be downloaded at https://github.com/researchmm/MM-Diffusion.