13 citations · 18 across the 8 of their papers we have counts for
8 papers
SIVA-RL: Sensitivity-Invariance Visual Alignment for Multimodal Reinforcement Learning
Cheng Tang, Junzhi Ning, Min Cen +9
Reinforcement learning with verifiable rewards (RLVR) drives multimodal reasoning, but answer-level correctness does not guarantee that a vision-language model grounds its predicti…
GraphCoT-VLA: A 3D Spatial-Aware Reasoning Vision-Language-Action Model for Robotic Manipulation with Ambiguous Instructions
Helong Huang, Min Cen, Kai Tan +3
Vision-language-action models have emerged as a crucial paradigm in robotic manipulation. However, existing VLA models exhibit notable limitations in handling ambiguous language in…
Constraint Multi-class Positive and Unlabeled Learning for Distantly Supervised Named Entity Recognition
Yuzhe Zhang, Min Cen, Hong Zhang
Distantly supervised named entity recognition (DS-NER) has been proposed to exploit the automatically labeled training data by external knowledge bases instead of human annotations…
DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture
Min Cen, Xingyu Li, Bangwei Guo +3
In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs). However, transformers are usually complex and reso…
Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelines
Min Cen, Xingyu Li, Bangwei Guo +3
NLP-based computer vision models, particularly vision transformers, have been shown to outperform CNN models in many imaging tasks. However, most digital pathology artificial-intel…
RAPS: A Novel Few-Shot Relation Extraction Pipeline with Query-Information Guided Attention and Adaptive Prototype Fusion
Yuzhe Zhang, Min Cen, Tongzhou Wu +1
Few-shot relation extraction (FSRE) aims at recognizing unseen relations by learning with merely a handful of annotated instances. To generalize to new relations more effectively,…
Colorectal cancer survival prediction using deep distribution based multiple-instance learning
Xingyu Li, Jitendra Jonnagaddala, Min Cen +2
Several deep learning algorithms have been developed to predict survival of cancer patients using whole slide images (WSIs).However, identification of image phenotypes within the W…
Improving Feature Extraction from Histopathological Images Through A Fine-tuning ImageNet Model
Xingyu Li, Min Cen, Jinfeng Xu +2
Due to lack of annotated pathological images, transfer learning has been the predominant approach in the field of digital pathology.Pre-trained neural networks based on ImageNet da…