We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly. X-Decodert takes as input two types of queries: (i) generic non-semantic queries and (ii) semantic queries induced from text inputs, to decode different pixel-level and token-level outputs in the same semantic space. With such a novel design, X-Decoder is the first work that provides a unified way to support all types of image segmentation and a variety of vision-language (VL) tasks. Further, our design enables seamless interactions across tasks at different granularities and brings mutual benefits by learning a common and rich pixel-level visual-semantic understanding space, without any pseudo-labeling. After pretraining on a mixed set of a limited amount of segmentation data and millions of image-text pairs, X-Decoder exhibits strong transferability to a wide range of downstream tasks in both zero-shot and finetuning settings. Notably, it achieves (1) state-of-the-art results on open-vocabulary segmentation and referring segmentation on eight datasets; (2) better or competitive finetuned performance to other generalist and specialist models on segmentation and VL tasks; and (3) flexibility for efficient finetuning and novel task composition (e.g., referring captioning and image editing). Code, demo, video, and visualization are available at https://x-decoder-vl.github.io.

Exploring dense matching between the current frame and past frames for long-range context modeling, memory-based methods have demonstrated impressive results in video object segmentation (VOS) recently. Nevertheless, due to the lack of instance understanding ability, the above approaches are oftentimes brittle to large appearance variations or viewpoint changes resulted from the movement of objects and cameras. In this paper, we argue that instance understanding matters in VOS, and integrating it with memory-based matching can enjoy the synergy, which is intuitively sensible from the definition of VOS task, \ie, identifying and segmenting object instances within the video. Towards this goal, we present a two-branch network for VOS, where the query-based instance segmentation (IS) branch delves into the instance details of the current frame and the VOS branch performs spatial-temporal matching with the memory bank. We employ the well-learned object queries from IS branch to inject instance-specific information into the query key, with which the instance-augmented matching is further performed. In addition, we introduce a multi-path fusion block to effectively combine the memory readout with multi-scale features from the instance segmentation decoder, which incorporates high-resolution instance-aware features to produce final segmentation results. Our method achieves state-of-the-art performance on DAVIS 2016/2017 val (92.6% and 87.1%), DAVIS 2017 test-dev (82.8%), and YouTube-VOS 2018/2019 val (86.3% and 86.3%), outperforming alternative methods by clear margins.

Benefiting from masked visual modeling, self-supervised video representation learning has achieved remarkable progress. However, existing methods focus on learning representations from scratch through reconstructing low-level features like raw pixel RGB values. In this paper, we propose masked video distillation (MVD), a simple yet effective two-stage masked feature modeling framework for video representation learning: firstly we pretrain an image (or video) model by recovering low-level features of masked patches, then we use the resulting features as targets for masked feature modeling. For the choice of teacher models, we observe that students taught by video teachers perform better on temporally-heavy video tasks, while image teachers transfer stronger spatial representations for spatially-heavy video tasks. Visualization analysis also indicates different teachers produce different learned patterns for students. Motivated by this observation, to leverage the advantage of different teachers, we design a spatial-temporal co-teaching method for MVD. Specifically, we distill student models from both video teachers and image teachers by masked feature modeling. Extensive experimental results demonstrate that video transformers pretrained with spatial-temporal co-teaching outperform models distilled with a single teacher on a multitude of video datasets. Our MVD with vanilla ViT achieves state-of-the-art performance compared with previous supervised or self-supervised methods on several challenging video downstream tasks. For example, with the ViT-Large model, our MVD achieves 86.4% and 75.9% Top-1 accuracy on Kinetics-400 and Something-Something-v2, outperforming VideoMAE by 1.2% and 1.6% respectively. Code will be available at \url{https://github.com/ruiwang2021/mvd}.

基于变压器的模型已在主要的视频识别基准上取得了最佳性能。与基于CNN的模型相比，这些模型受益于自我发项机制，显示出更强的建模长期依赖性能力。但是，大量的计算开销是由于自我注意力的二次复杂性在大量令牌之上，限制了现有的视频变压器在具有有限资源（例如移动设备）的应用程序中的使用。在本文中，我们将移动格式扩展到视频移动格式，该版本将视频体系结构分解为轻量级的3D-CNN，用于本地上下文建模，并以并行方式将变压器模块用于全局交互建模。为了避免通过计算视频中大量本地补丁之间的自我注意力而产生的重大计算成本，我们建议在变形金刚中使用很少的全球令牌（例如6）将整个视频中的整个视频用于与3D-CNN交换信息 - 注意机制。通过有效的全球时空建模，视频移动形式显着提高了替代轻型基线的视频识别性能，并且在各种视频识别任务上，低FLOP策略的其他有效CNN模型从500m到6G总鞋类胜过其他基于CNN的模型。值得注意的是，视频移动格式是第一个基于变压器的视频模型，它限制了1G失败范围内的计算预算。

对象检测器的复杂性过度权衡是资源约束视觉任务的关键问题。先前的作品强调了用有效的骨干实现的检测器。在这项工作中，研究了对检测负责人对提案处理的这种权衡的影响。假设提高的检测效率需要范式转移，朝着不平等的建议处理，将更多的计算分配给良好的建议，而不是贫穷的建议。这可以更好地利用可用的计算预算，从而为同一失败提供了更高的精度。我们将其作为一个学习问题提出，目的是将操作员分配给检测头的建议，以便将总计算成本受到限制，并且精确度最大。关键发现是，可以将这种匹配作为一个函数，该函数将每个提案嵌入到操作员的单速代码中。尽管此功能诱导了复杂的动态网络路由机制，但它可以由简单的MLP实现，并通过现成的对象检测器端到端学习。这种“动态建议处理”（DPP）显示出明确的计算复杂性的明确余量，表现出优于最先进的端到端对象检测器（DETR，稀疏R-CNN）。

我们提出了GLIPV2，这是一个接地的VL理解模型，该模型既服务于本地化任务（例如，对象检测，实例分割）和视觉语言（VL）理解任务（例如VQA，图像字幕）。 GLIPV2优雅地将本地化预训练和视觉语言预训练（VLP）具有三个预训练任务：短语接地作为对检测任务的VL重新重新制定，区域词对比度学习作为新型的区域词对比度对比度对比学习任务，以及蒙面的语言建模。这种统一不仅简化了先前的多阶段VLP程序，而且还可以在本地化和理解任务之间实现相互利益。实验结果表明，在各种本地化和理解任务上，单个GLIPV2模型（所有模型权重）在SOTA性能附近实现。该模型还显示了（1）在开放式摄制对象检测任务上进行的强零射击和很少的自适应性能，以及（2）VL理解任务上的卓越接地能力。代码将在https://github.com/microsoft/glip上发布。

利用大规模数据可以在许多计算机视觉任务上引入性能增长。不幸的是，当对象检测中训练多个数据集下的单个模型时，这并没有发生。我们观察到两个主要障碍：分类学差异和边界框注释不一致，这引入了不同数据集中的域间隙，从而阻止我们进行联合培训。在本文中，我们表明，可以通过简单地将对象查询在每个数据集的类别嵌入语言嵌入中来有效地解决这两个挑战。我们设计一个检测中心以根据数据集的不同分布在类别嵌入中动态调整查询。与以前的方法试图学习所有数据集的联合嵌入方式不同，我们的适应方法可以利用语言嵌入作为通用类别的语义中心，同时学习对属于不同数据集的特定类别的语义偏见来处理注释差异并弥补域间隙。这些新颖的改进使我们能够同时在多个数据集上端到端培训单个探测器，以充分利用它们的优势。在多个数据集上进行联合培训的进一步实验证明了对单独的单个微型检测器的显着性能提高。

人们说：“一张照片值一千字”。那么，我们如何从图像中获取丰富的信息？我们认为，通过使用视觉线索来桥接大型的识别视觉基础模型和语言模型，我们可以无需任何额外的跨模式训练。得益于基础模型的强大零拍功能，我们首先构建图像的丰富语义表示（例如，图像标签，对象属性 /位置，字幕）作为结构化的文本提示，称为视觉线索，使用视觉基础模型。基于视觉线索，我们使用大型语言模型为视觉内容生成一系列综合描述，然后再次通过视觉模型验证，以选择与图像最合适的候选人。我们通过定量和定性测量评估生成的描述的质量。结果证明了这种结构化语义表示的有效性。

专家（MOE）的混合物能够有效地扩展视觉变压器。但是，它需要禁止计算资源来训练大型MOE变压器。在本文中，我们提出了专家的残留混合物（RMOE），这是在下游任务（例如分割和检测）上针对MOE视觉变压器的有效训练管道。 RMOE通过上限的MOE培训获得了可比的结果，而仅引入较小的额外培训成本，而不是较低的非MOE训练管道。效率得到了我们的关键观察的支持：MOE变压器的权重可以纳入无独立的核心和输入依赖性残差。与重量核心相比，可以通过更少的计算资源（例如，在下游数据上进行填充）进行有效训练重量。我们表明，与当前的MOE培训管道相比，我们获得了可比的结果，同时节省了30％以上的培训成本。与最先进的非MOE变压器（例如SWIN-T / CVT-13 / SWIN-L）相比，我们在ADE20K分割方面获得+1.1 / 0.9 / 1.0 MIOU的增益，+1.4 / 1.6 / 0.6 / 0.6 AP获得MS-Coco对象检测任务，额外培训成本不到3％。

We propose focal modulation networks (FocalNets in short), where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision. Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation. Specifically, FocalNets with tiny and base size achieve 82.3% and 83.9% top-1 accuracy on ImageNet-1K. After pretrained on ImageNet-22K in 224 resolution, it attains 86.5% and 87.3% top-1 accuracy when finetuned with resolution 224 and 384, respectively. When transferred to downstream tasks, FocalNets exhibit clear superiority. For object detection with Mask R-CNN, FocalNet base trained with 1\times outperforms the Swin counterpart by 2.1 points and already surpasses Swin trained with 3\times schedule (49.0 v.s. 48.5). For semantic segmentation with UPerNet, FocalNet base at single-scale outperforms Swin by 2.4, and beats Swin at multi-scale (50.5 v.s. 49.7). Using large FocalNet and Mask2former, we achieve 58.5 mIoU for ADE20K semantic segmentation, and 57.9 PQ for COCO Panoptic Segmentation. Using huge FocalNet and DINO, we achieved 64.3 and 64.4 mAP on COCO minival and test-dev, respectively, establishing new SoTA on top of much larger attention-based models like Swinv2-G and BEIT-3. Code and checkpoints are available at https://github.com/microsoft/FocalNet.