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Found 4 papers, 1 papers with code
Exploring Optimal Transport-Based Multi-Grained Alignments for Text-Molecule Retrieval
In this work, we introduce the Optimal TRansport-based Multi-grained Alignments model (ORMA), a novel approach that facilitates multi-grained alignments between textual descriptions and molecules.
Ranked #1 on
Cross-Modal Retrieval
on ChEBI-20
Retrieval-augmented Multi-modal Chain-of-Thoughts Reasoning for Large Language Models
The advancement of Large Language Models (LLMs) has brought substantial attention to the Chain of Thought (CoT) approach, primarily due to its ability to enhance the capability of LLMs on complex reasoning tasks.
On the Cultural Gap in Text-to-Image Generation
Accordingly, we propose a novel multi-modal metric that considers object-text alignment to filter the fine-tuning data in the target culture, which is used to fine-tune a T2I model to improve cross-cultural generation.
Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration
Although instruction-tuned large language models (LLMs) have exhibited remarkable capabilities across various NLP tasks, their effectiveness on other data modalities beyond text has not been fully studied.
