Towards General Purpose Vision Systems (2104.00743v2)

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 non-trivial 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 transfer, and learning efficiency that may inform future 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.

• The paper presents a task-agnostic vision-language architecture that unifies multiple vision tasks without any structural modifications.
• It demonstrates sample-efficient learning with impressive zero-shot performance on tasks like referring expressions.
• The model leverages natural language guidance to generalize learned concepts across diverse tasks, simplifying multi-task learning.

Towards General-Purpose Vision Systems: An Insightful Overview

The paper "Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture" aims to tackle one of the persistent challenges in computer vision: the development of a versatile model capable of handling a wide array of vision-related tasks without the need for architectural modifications. This challenge is addressed by the proposed task-agnostic vision-language architecture, which simplifies the process of extending a model's capability to new tasks.

Motivation and Objectives

Traditionally, computer vision systems are confined to a limited number of predefined tasks, requiring either architectural alterations or retraining when confronted with new tasks. This limitation prompts the need for general-purpose systems that can efficiently learn and execute a variety of vision tasks. The proposed system, which can interpret an image and generate corresponding annotations in terms of text and/or bounding boxes, aims to solve tasks such as classification, localization, visual question answering (VQA), and captioning within a unified framework.

Core Contributions

The paper introduces a novel architecture that is guided by natural language task descriptions and uses a shared set of vision and language modules across tasks. This integration effectively enables the model to tackle:

• Generality of Architecture: The framework accommodates a wide variety of tasks by allowing task-specific inputs and outputs through natural language without necessitating any modifications to its initial structure.
• Concept Generalization Across Skills: The model demonstrates the capacity to transfer learned knowledge of concepts from one task to another, illustrating its potential for skill-concept generalization.
• Learning Efficiency: The model showcases sample-efficient learning and demonstrates superior zero-shot task performance when tested with novel tasks, such as referring expressions, without prior specific training.

Experimental Results and Evaluation

Experiments were conducted on several benchmark datasets, particularly focusing on tasks derived from the COCO and VQA datasets. The model exhibits competitive performance relative to specialized models when handling individual tasks and surpasses them when jointly trained on multiple tasks. Notably, it achieves a significant boost in zero-shot learning scenarios, with the ability to improve the referring expression task performance by fine-tuning on a small number of training samples.

The introduction of the COCO-SCE (Skill-Concept Evaluation) split offers a robust framework for evaluating concept generalization across tasks. The model achieved marked improvements in performance on unseen skill-concept combinations, indicating significant advancements in generalization capabilities.

Implications and Future Scope

The implications of this research are substantial for the future development of computer vision systems. A move towards general-purpose architectures could drastically reduce the effort and complexity involved in developing vision systems for new domains, paving the way for more flexible and adaptive AI solutions. Furthermore, this paper's methodology may inspire future work focused on expanding the modality of inputs and outputs, including video data or point cloud interpretations, thereby broadening the application spectrum of such systems.

Challenges and Final Thoughts

While the model presents an effective approach to general-purpose vision systems, challenges remain, particularly in balancing computational efficiency and maintaining high performance across numerous tasks. Additionally, like many AI models, this system inherits the ethical challenges stemming from dataset biases and energy consumption concerns. Hence, ongoing research will need to address these concerns to responsibly harness the potential of general-purpose AI systems.

In conclusion, this paper provides a solid foundation for developing vision systems that are adaptable and efficient across tasks, supporting broader AI research objectives of creating more autonomous and intelligent systems.