1 citations · 1 across the 1 of their papers we have counts for
4 papers
UniME-V2: MLLM-as-a-Judge for Universal Multimodal Embedding Learning
Tiancheng Gu, Kaicheng Yang, Kaichen Zhang +6
Universal multimodal embedding models are foundational to various tasks. Existing approaches typically employ in-batch negative mining by measuring the similarity of query-candidat…
Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs
Tiancheng Gu, Kaicheng Yang, Ziyong Feng +6
The Contrastive Language-Image Pre-training (CLIP) framework has become a widely used approach for multimodal representation learning, particularly in image-text retrieval and clus…
RealSyn: An Effective and Scalable Multimodal Interleaved Document Transformation Paradigm
Tiancheng Gu, Kaicheng Yang, Chaoyi Zhang +6
After pre-training on extensive image-text pairs, Contrastive Language-Image Pre-training (CLIP) demonstrates promising performance on a wide variety of benchmarks. However, a subs…
ORID: Organ-Regional Information Driven Framework for Radiology Report Generation
Tiancheng Gu, Kaicheng Yang, Xiang An +3
The objective of Radiology Report Generation (RRG) is to automatically generate coherent textual analyses of diseases based on radiological images, thereby alleviating the workload…