9 papers
CL-CLIP: CLIP-Based Continual Learning Framework with Cost-Volume Category Decoupling for Object Detection
Zihan Liu, Yuguang Yang, Shengjie Su +5
Continual Object Detection (COD) requires a detector to acquire new categories over time while preserving previously learned ones. This goal is closely related to open-vocabulary d…
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…
Generation-Augmented Generation: A Plug-and-Play Framework for Private Knowledge Injection in Large Language Models
Rongji Li, Jian Xu, Yi Chen +7
In domains such as materials science, biomedicine, and finance, high-stakes deployment of large language models (LLMs) requires injecting private, domain-specific knowledge that is…
AMLRIS: Alignment-aware Masked Learning for Referring Image Segmentation
Tongfei Chen, Shuo Yang, Yuguang Yang +7
Referring Image Segmentation (RIS) aims to segment the object in an image uniquely referred to by a natural language expression. However, RIS training often contains hard-to-align…
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…
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…