Search Results for author:
Found 8 papers, 3 papers with code
Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models
We propose a novel method that utilizes the extensive knowledge contained within a large corpus of scientific literature to deduce causal relationships in general causal graph recovery tasks.
CANAMRF: An Attention-Based Model for Multimodal Depression Detection
Multimodal depression detection is an important research topic that aims to predict human mental states using multimodal data.
Diffusion-based Blind Text Image Super-Resolution
Since text prior is important to guarantee the correctness of the restored text structure according to existing arts, we also propose a Text Diffusion Model (TDM) for text recognition which can guide IDM to generate text images with correct structures.
Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data
However, there exists a significant gap between synthetic and real-world data, which hinders the generalization of neural models trained on these synthetic datasets.
RAPS: A Novel Few-Shot Relation Extraction Pipeline with Query-Information Guided Attention and Adaptive Prototype Fusion
Few-shot relation extraction (FSRE) aims at recognizing unseen relations by learning with merely a handful of annotated instances.
rPPG-Toolbox: Deep Remote PPG Toolbox
Camera-based physiological measurement is a fast growing field of computer vision.
FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigm
We conduct experiments on a publicly available Chinese financial dataset ChFinAnn and a real-word bussiness dataset AdminPunish.
