Search Results for author:
Found 6 papers, 2 papers with code
CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting Authentication
In this paper, we propose a novel Contrastive Self-Supervised Learning framework for Robust Handwriting Authentication (CSSL-RHA) to address these issues.
Towards Visual Question Answering on Pathology Images
In this paper, we aim to develop a pathological visual question answering framework to analyze pathology images and answer medical questions related to these images.
Pathological Visual Question Answering
To deal with the issue that a publicly available pathology VQA dataset is lacking, we create PathVQA dataset.
TreeGAN: Incorporating Class Hierarchy into Image Generation
Based on these two ideas, we propose a TreeGAN model which consists of three modules: (1) a class hierarchy encoder (CHE) which takes the hierarchical structure of classes and their textual names as inputs and learns an embedding for each class; the embedding captures the hierarchical relationship among classes; (2) a conditional image generator (CIG) which takes the CHE-generated embedding of a class as input and generates a set of images belonging to this class; (3) a consistency checker which performs hierarchical classification on the generated images and checks whether the generated images are compatible with the class hierarchy; the consistency score is used to guide the CIG to generate hierarchy-compatible images.
PathVQA: 30000+ Questions for Medical Visual Question Answering
To achieve this goal, the first step is to create a visual question answering (VQA) dataset where the AI agent is presented with a pathology image together with a question and is asked to give the correct answer.
Deep Determinantal Point Process for Large-Scale Multi-Label Classification
Experiments on the two datasets demonstrate the efficacy and efficiency of the proposed methods.
