Search Results for author:
Found 21 papers, 8 papers with code
MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 FLOPs
In addition, combining CNN and Transformer can effectively combine global and local information for enhancement.
FLDM-VTON: Faithful Latent Diffusion Model for Virtual Try-on
Despite their impressive generative performance, latent diffusion model-based virtual try-on (VTON) methods lack faithfulness to crucial details of the clothes, such as style, pattern, and text.
MedRG: Medical Report Grounding with Multi-modal Large Language Model
Medical Report Grounding is pivotal in identifying the most relevant regions in medical images based on a given phrase query, a critical aspect in medical image analysis and radiological diagnosis.
Part-Attention Based Model Make Occluded Person Re-Identification Stronger
However, occluded person ReID still suffers from background clutter and low-quality local feature representations, which limits model performance.
Occluded Cloth-Changing Person Re-Identification
We define cloth-changing person re-identification in occlusion scenarios as occluded cloth-changing person re-identification (Occ-CC-ReID), and to the best of our knowledge, we are the first to propose occluded cloth-changing person re-identification as a new task.
Low-dose CT Denoising with Language-engaged Dual-space Alignment
While various deep learning methods were proposed for low-dose computed tomography (CT) denoising, they often suffer from over-smoothing, blurring, and lack of explainability.
Exploiting Emotion-Semantic Correlations for Empathetic Response Generation
Based on dynamic emotion-semantic vectors and dependency trees, we propose a dynamic correlation graph convolutional network to guide the model in learning context meanings in dialogue and generating empathetic responses.
Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices
Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data.
IQAGPT: Image Quality Assessment with Vision-language and ChatGPT Models
Second, we fine-tune the image quality captioning VLM on the CT-IQA dataset to generate quality descriptions.
AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluation
In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics.
ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising
In this paper, we propose a novel Anatomy-aware Supervised CONtrastive learning framework, termed ASCON, which can explore the anatomical semantics for low-dose CT denoising while providing anatomical interpretability.
Learning Physical-Spatio-Temporal Features for Video Shadow Removal
Then, we develop a progressive aggregation module to enhance the spatio and temporal characteristics of features maps, and effectively integrate the three kinds of features.
Medical Phrase Grounding with Region-Phrase Context Contrastive Alignment
To enable MedRPG to locate nuanced medical findings with better region-phrase correspondences, we further propose Tri-attention Context contrastive alignment (TaCo).
LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring
Here, we propose to link in-plane and through-plane transformers for simultaneous in-plane denoising and through-plane deblurring, termed as LIT-Former, which can efficiently synergize in-plane and through-plane sub-tasks for 3D CT imaging and enjoy the advantages of both convolution and transformer networks.
A Review of Uncertainty Estimation and its Application in Medical Imaging
We further discuss how they can be estimated in medical imaging.
Feature Transformation for Cross-domain Few-shot Remote Sensing Scene Classification
Moreover, FTM can be effectively learned on target domain in the case of few training data available and is agnostic to specific network structures.
Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department.
Physics-informed generative neural network: an application to troposphere temperature prediction
The generative neural network takes the mask as prior for the second-stage refined predictions.
Triple-cooperative Video Shadow Detection
The bottleneck is the lack of a well-established dataset with high-quality annotations for video shadow detection.
A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection
To boost the shadow detection performance, this paper presents a multi-task mean teacher model for semi-supervised shadow detection by leveraging unlabeled data and exploring the learning of multiple information of shadows simultaneously.
Ranked #1 on
Shadow Detection
on SBU
(using extra training data)
Effects of Blur and Deblurring to Visual Object Tracking
In this paper, we address these two problems by constructing a Blurred Video Tracking benchmark, which contains a variety of videos with different levels of motion blurs, as well as ground truth tracking results for evaluating trackers.
