arXiv:2305.14985v3 Announce Type: replace-cross Abstract: The field of vision-and-language (VL) understanding has made unprecedented progress with end-to-end large pre-trained VL models (VLMs). However, they still fall short in zero-shot reasoning tasks that require multi-step inferencing. To achieve this goal, previous works resort to a divide-and-conquer pipeline. In this paper, we argue that previous efforts have several inherent shortcomings: 1) They rely on domain-specific sub-question decomposing models. 2) They force models to predict the final answer even if the sub-questions or sub-an
The rapid advancement in large language models has exposed limitations in their multi-step reasoning abilities, particularly for complex vision-language tasks, prompting new architectural approaches.
This research outlines a method for improving the zero-shot reasoning capabilities of large vision-language models, which is critical for their autonomous functioning in diverse applications.
The proposed 'IdealGPT' framework seeks to overcome current VLM shortcomings in multi-step inferencing by iteratively decomposing vision and language reasoning, moving beyond domain-specific sub-question models.
- · AI developers
- · Robotics
- · Autonomous systems
- · Generative AI platforms
- · Current end-to-end VLM architectures without decomposition
- · Domain-specific AI models for sub-tasks
Improved performance of large vision-language models in complex reasoning tasks, enabling more sophisticated applications.
Accelerated development of AI agents capable of more autonomous and nuanced interaction with their environment.
Enhanced AI capabilities could reduce the need for human intervention in complex visual and linguistic decision-making workflows across various industries.
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