4 citations · 12 across the 21 of their papers we have counts for
7 papers · 1 filter
Rethinking Causal Relationships Learning in Graph Neural Networks
Hang Gao, Chengyu Yao, Jiangmeng Li +5
Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling complex interrelationships within graph-structured data. To enhance the credibility and robustne…
UIEDP:Underwater Image Enhancement with Diffusion Prior
Dazhao Du, Enhan Li, Lingyu Si +3
Underwater image enhancement (UIE) aims to generate clear images from low-quality underwater images. Due to the unavailability of clear reference images, researchers often synthesi…
PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification
Meijuan Su, Feihong He, Fanzhang Li
Few-shot image classification has received considerable attention for overcoming the challenge of limited classification performance with limited samples in novel classes. Most exi…
Cartoondiff: Training-free Cartoon Image Generation with Diffusion Transformer Models
Feihong He, Gang Li, Lingyu Si +4
Image cartoonization has attracted significant interest in the field of image generation. However, most of the existing image cartoonization techniques require re-training models u…
Background Debiased SAR Target Recognition via Causal Interventional Regularizer
Hongwei Dong, Fangzhou Han, Lingyu Si +2
Recent studies have utilized deep learning (DL) techniques to automatically extract features from synthetic aperture radar (SAR) images, which shows great promise for enhancing the…
A Dimensional Structure based Knowledge Distillation Method for Cross-Modal Learning
Lingyu Si, Hongwei Dong, Wenwen Qiang +5
Due to limitations in data quality, some essential visual tasks are difficult to perform independently. Introducing previously unavailable information to transfer informative dark…
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective
Hang Gao, Jiangmeng Li, Wenwen Qiang +5
Benefiting from the injection of human prior knowledge, graphs, as derived discrete data, are semantically dense so that models can efficiently learn the semantic information from…