4 accepted papers
Prompt tuning vision-language models like CLIP has shown great potential in learning transferable representations for various downstream tasks. The main issue is how to mitigate the over-fitting problem on downstream tasks with limited training samples. While knowledge-guided context optimization ha…
Cross-modal retrieval is an important yet challenging task due to the semantic discrepancy between visual content and language. To measure the correlation between images and text, most existing research mainly focuses on learning global or local correspondence, failing to explore fine-grained local-…
Large language models (LLMs) have demonstrated remarkable performance in various natural language processing tasks. However, they still face significant challenges in automated reasoning, particularly in scenarios involving multi-step reasoning. In this paper, we focus on the logical reasoning probl…
Metaphors are linguistic expressions that convey non-literal meanings, as well as cognitive mappings that establish connections between distinct domains of experience or knowledge. This paper proposes a novel formulation of metaphor identification as a relation extraction problem. We introduce meta…