9 citations · 9 across the 11 of their papers we have counts for
5 papers · 1 filter
RzenEmbed: Towards Comprehensive Multimodal Retrieval
Weijian Jian, Yajun Zhang, Dawei Liang +4
The rapid advancement of Multimodal Large Language Models (MLLMs) has extended CLIP-based frameworks to produce powerful, universal embeddings for retrieval tasks. However, existin…
FG-CLIP 2: A Bilingual Fine-grained Vision-Language Alignment Model
Chunyu Xie, Bin Wang, Fanjing Kong +5
Fine-grained vision-language understanding requires precise alignment between visual content and linguistic descriptions, a capability that remains limited in current models, parti…
LMM-Det: Make Large Multimodal Models Excel in Object Detection
Jincheng Li, Chunyu Xie, Ji Ao +2
Large multimodal models (LMMs) have garnered wide-spread attention and interest within the artificial intelligence research and industrial communities, owing to their remarkable ca…
FG-CLIP: Fine-Grained Visual and Textual Alignment
Chunyu Xie, Bin Wang, Fanjing Kong +5
Contrastive Language-Image Pre-training (CLIP) excels in multimodal tasks such as image-text retrieval and zero-shot classification but struggles with fine-grained understanding du…
Prompt as Knowledge Bank: Boost Vision-language model via Structural Representation for zero-shot medical detection
Yuguang Yang, Tongfei Chen, Haoyu Huang +5
Zero-shot medical detection can further improve detection performance without relying on annotated medical images even upon the fine-tuned model, showing great clinical value. Rece…