3 papers
cs.CV2026
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…
cs.CV2025
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…
cs.CV2025
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…