5 citations · 8 across the 9 of their papers we have counts for
10 papers
A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects
Shulan Ruan, Rongwei Wang, Xuchen Shen +8
Multi-sensor fusion perception (MSFP) is a key technology for embodied AI, which can serve a variety of downstream tasks (e.g., 3D object detection and semantic segmentation) and a…
SentiFormer: Metadata Enhanced Transformer for Image Sentiment Analysis
Bin Feng, Shulan Ruan, Mingzheng Yang +4
As more and more internet users post images online to express their daily emotions, image sentiment analysis has attracted increasing attention. Recently, researchers generally ten…
Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models
Liyang He, Chenglong Liu, Rui Li +4
Sentence embedding is essential for many NLP tasks, with contrastive learning methods achieving strong performance using annotated datasets like NLI. Yet, the reliance on manual la…
Predictive Accuracy-Based Active Learning for Medical Image Segmentation
Jun Shi, Shulan Ruan, Ziqi Zhu +4
Active learning is considered a viable solution to alleviate the contradiction between the high dependency of deep learning-based segmentation methods on annotated data and the exp…
H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation
Jun Shi, Hongyu Kan, Shulan Ruan +6
Recently, deep learning methods have been widely used for tumor segmentation of multimodal medical images with promising results. However, most existing methods are limited by insu…
Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer
Xiangneng Gao, Shulan Ruan, Jun Shi +2
In the treatment of ovarian cancer, precise residual disease prediction is significant for clinical and surgical decision-making. However, traditional methods are either invasive (…
Description-Enhanced Label Embedding Contrastive Learning for Text Classification
Kun Zhang, Le Wu, Guangyi Lv +6
Text Classification is one of the fundamental tasks in natural language processing, which requires an agent to determine the most appropriate category for input sentences. Recently…
DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis
Shulan Ruan, Yong Zhang, Kun Zhang +4
Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency. Previous methods usually…
DARNet: Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 via CT Images
Jun Shi, Huite Yi, Shulan Ruan +4
The ongoing global pandemic of Coronavirus Disease 2019 (COVID-19) poses a serious threat to public health and the economy. Rapid and accurate diagnosis of COVID-19 is crucial to p…
R-Net: Relation of Relation Learning Network for Sentence Semantic Matching
Kun Zhang, Le Wu, Guangyi Lv +3
Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recentl…