Ma, T.; Organisciak, D.; Ma, W.; Long, Y. Towards Cognition-Aligned Visual Language Models via Zero-Shot Instance Retrieval. Electronics2024, 13, 1660.
Ma, T.; Organisciak, D.; Ma, W.; Long, Y. Towards Cognition-Aligned Visual Language Models via Zero-Shot Instance Retrieval. Electronics 2024, 13, 1660.
Ma, T.; Organisciak, D.; Ma, W.; Long, Y. Towards Cognition-Aligned Visual Language Models via Zero-Shot Instance Retrieval. Electronics2024, 13, 1660.
Ma, T.; Organisciak, D.; Ma, W.; Long, Y. Towards Cognition-Aligned Visual Language Models via Zero-Shot Instance Retrieval. Electronics 2024, 13, 1660.
Abstract
The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can integrate seamlessly into societal frameworks requiring nuanced understanding and decision-making. Zero-Shot Instance Retrieval (ZSIR) stands at the forefront of this endeavour, potentially providing a robust platform for AI systems, particularly large visual language models, to demonstrate and refine cognition-aligned learning without the need for direct experience. In this paper, we critically evaluate current cognition alignment methodologies within traditional zero-shot learning paradigms, using visual attributes and word embedding generated by large AI models. We propose a unified similarity function that quantifies the cognitive alignment level, bridging the gap between AI processes and human-like understanding. Through extensive experimentation, our findings illustrate that this similarity function can effectively mirror the visual-semantic gap, steering the model towards enhanced performance in zero-shot instance retrieval. This work not only benchmarks the cognition alignment of AI, but also sets a new precedent for the development of visual language models attuned to the complexities of human cognition.}
Keywords
Large Visual Language Models; Zero-Shot Instance Retrieval; Cognition-Alignment
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
