From the 1 of 49 papers with an AI index.
35 citations
- National University of SingaporeSG3 papers
- Peking UniversityCN3 papers
- Peng Huanwu Center for Fundamental TheoryCN3 papers
- Tsinghua UniversityCN3 papers
- Chinese Academy of SciencesCN2 papers
- Loughborough UniversityGB2 papers
- Shandong UniversityCN2 papers
- Tianjin International Joint Academy of BiomedicineCN2 papers
- Tianjin University of TechnologyCN2 papers
- Agency for Science, Technology and ResearchSG1 paper
- Arizona State UniversityUS1 paper
- Baidu (China)CN1 paper
5 papers · 1 filter
Domain-Division based Progressive Learning for Source-Free Domain Adaptation
Pan Liu, Jing Li, Meng Zhao +3
With growing privacy and portability concerns, source-free domain adaptation requires only a source pre-trained model and an unlabeled target domain, allowing for effective adaptat…
VaaWIT: Visual-Aware Adaptation of Large Language Models for Multilingual Web Image Translation
Bo Li, Ronghao Chen, Ningyuan Deng +3
Translating text embedded in Web images is crucial for improving content accessibility and cross-lingual information retrieval, particularly within social media and e-commerce doma…
Enhancing 3D Semantic Scene Completion with a Refinement Module
Dunxing Zhang, Jiachen Lu, Han Yang +2
We propose ESSC-RM, a plug-and-play Enhancing framework for Semantic Scene Completion with a Refinement Module, which can be seamlessly integrated into existing SSC models. ESSC-RM…
Structure Causal Models and LLMs Integration in Medical Visual Question Answering
Zibo Xu, Qiang Li, Weizhi Nie +2
Medical Visual Question Answering (MedVQA) aims to answer medical questions according to medical images. However, the complexity of medical data leads to confounders that are diffi…
Dual Contrastive Network for Few-Shot Remote Sensing Image Scene Classification
Zhong Ji, Liyuan Hou, Xuan Wang +2
Few-shot remote sensing image scene classification (FS-RSISC) aims at classifying remote sensing images with only a few labeled samples. The main challenges lie in small inter-clas…