151 citations · 650 across the 34 of their papers we have counts for
11 papers · 1 filter
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities
Wei Ma, Shangqing Liu, Mengjie Zhao +5
Past research has examined how well these models grasp code syntax, yet their understanding of code semantics still needs to be explored. We extensively analyze seven code models t…
Learning to Learn Domain-invariant Parameters for Domain Generalization
Feng Hou, Yao Zhang, Yang Liu +6
Due to domain shift, deep neural networks (DNNs) usually fail to generalize well on unknown test data in practice. Domain generalization (DG) aims to overcome this issue by capturi…
SAP-DETR: Bridging the Gap Between Salient Points and Queries-Based Transformer Detector for Fast Model Convergency
Yang Liu, Yao Zhang, Yixin Wang +5
Recently, the dominant DETR-based approaches apply central-concept spatial prior to accelerate Transformer detector convergency. These methods gradually refine the reference points…
Improving Radiology Summarization with Radiograph and Anatomy Prompts
Jinpeng Hu, Zhihong Chen, Yang Liu +2
The impression is crucial for the referring physicians to grasp key information since it is concluded from the findings and reasoning of radiologists. To alleviate the workload of…
Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training
Zhihong Chen, Yuhao Du, Jinpeng Hu +4
Medical vision-and-language pre-training provides a feasible solution to extract effective vision-and-language representations from medical images and texts. However, few studies h…
CommitBART: A Large Pre-trained Model for GitHub Commits
Shangqing Liu, Yanzhou Li, Xiaofei Xie +1
GitHub commits, which record the code changes with natural language messages for description, play a critical role for software developers to comprehend the software evolution. To…
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images
Yao Zhang, Jiawei Yang, Yang Liu +6
Purpose: Automated liver tumor segmentation from Computed Tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. How…
Hero-Gang Neural Model For Named Entity Recognition
Jinpeng Hu, Yaling Shen, Yang Liu +2
Named entity recognition (NER) is a fundamental and important task in NLP, aiming at identifying named entities (NEs) from free text. Recently, since the multi-head attention mecha…
Self-supervised Contrastive Learning for Audio-Visual Action Recognition
Yang Liu, Ying Tan, Haoyuan Lan
The underlying correlation between audio and visual modalities can be utilized to learn supervised information for unlabeled videos. In this paper, we propose an end-to-end self-su…
Learning Program Semantics with Code Representations: An Empirical Study
Jing Kai Siow, Shangqing Liu, Xiaofei Xie +2
Program semantics learning is the core and fundamental for various code intelligent tasks e.g., vulnerability detection, clone detection. A considerable amount of existing works pr…
Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning
Eduardo R. Corral-Soto, Mrigank Rochan, Yannis Y. He +3
In this paper we address the challenging problem of domain adaptation in LiDAR semantic segmentation. We consider the setting where we have a fully-labeled data set from source dom…