Towards Cognition-Aligned Visual Language Models via Zero-Shot Instance Retrieval

The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can seamlessly integrate 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. Our models achieve state-of-the-art performance on both the SUN (92.8% and 82.2%) and CUB datasets (59.92% and 48.82%) for bi-directional image-attribute retrieval accuracy. 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.