论文标题
视觉任务的统一序列接口
A Unified Sequence Interface for Vision Tasks
论文作者
论文摘要
尽管语言任务自然而然地以单个，统一的建模框架（即生成代币序列）表示，但在计算机视觉中并非如此。结果，对于不同的视力任务，不同的架构和损失功能的扩散。在这项工作中，我们表明，如果根据共享像素到序列接口进行配合，则可以统一一组“核心”计算机视觉任务。我们专注于四个任务，即对象检测，实例分割，关键点检测和图像字幕，所有这些任务都具有各种类型的输出，例如边界框或密集的掩码。尽管如此，通过将每个任务的输出作为具有统一界面的离散令牌的顺序，我们表明可以在所有这些任务上训练具有单个模型体系结构和损失功能的神经网络，而没有特定于任务的自定义。为了解决特定任务，我们使用一个简短的提示作为任务说明，序列输出适应了提示，以便它可以产生特定于任务的输出。我们表明，与成熟的特定任务模型相比，这种模型可以实现竞争性能。
While language tasks are naturally expressed in a single, unified, modeling framework, i.e., generating sequences of tokens, this has not been the case in computer vision. As a result, there is a proliferation of distinct architectures and loss functions for different vision tasks. In this work we show that a diverse set of "core" computer vision tasks can also be unified if formulated in terms of a shared pixel-to-sequence interface. We focus on four tasks, namely, object detection, instance segmentation, keypoint detection, and image captioning, all with diverse types of outputs, e.g., bounding boxes or dense masks. Despite that, by formulating the output of each task as a sequence of discrete tokens with a unified interface, we show that one can train a neural network with a single model architecture and loss function on all these tasks, with no task-specific customization. To solve a specific task, we use a short prompt as task description, and the sequence output adapts to the prompt so it can produce task-specific output. We show that such a model can achieve competitive performance compared to well-established task-specific models.
