Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture

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Abstract

Computer vision systems today are primarily N-purpose systems, designed and trained for a predefined set of tasks. Adapting such systems to new tasks is challenging and often requires nontrivial modifications to the network architecture (e.g. adding new output heads) or training process (e.g. adding new losses). To reduce the time and expertise required to develop new applications, we would like to create general purpose vision systems that can learn and perform a range of tasks without any modification to the architecture or learning process. In this paper, we propose GPV-1, a task-agnostic vision-language architecture that can learn and perform tasks that involve receiving an image and producing text and/or bounding boxes, including classification, localization, visual question answering, captioning, and more. We also propose evaluations of generality of architecture, skill-concept 1 1 For this work, we define concepts, skills and tasks as follows: Concepts - nouns (e.g. car, person, dog), Skills - operations that we wish to perform on the given inputs (e.g. classification, object detection, image captioning), Tasks - predefined combinations of a set of skills performed on a set of concepts (e.g. ImageNet classification task involves the skill of image classification across 1000 concepts). transfer, and learning efficiency that may informfuture work on general purpose vision. Our experiments indicate GPV-1 is effective at multiple tasks, reuses some concept knowledge across tasks, can perform the Referring Expressions task zero-shot, and further improves upon the zero-shot performance using a few training samples.

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