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.

本文提出一种基于共享像素到序列接口的框架，将计算机视觉中的四种不同任务（对象检测、实例分割、关键点检测和图像字幕）的输出形式都改为序列输出，并且可以通过短提示来实现任务特定的输出，证明可以用单个模型架构和损失函数在这些任务上进行训练，达到与传统模型相似的性能表现。