Search Results for author:
Found 27 papers, 18 papers with code
LPUWF-LDM: Enhanced Latent Diffusion Model for Precise Late-phase UWF-FA Generation on Limited Dataset
Our Latent Diffusion Model for Ultra-Wide-Field Late-Phase Fluorescein Angiography (LPUWF-LDM) effectively reconstructs fine details in late-phase UWF-FA and achieves state-of-the-art results compared to other existing methods when working with limited datasets.
Toward Robust Early Detection of Alzheimer's Disease via an Integrated Multimodal Learning Approach
Alzheimer's Disease (AD) is a complex neurodegenerative disorder marked by memory loss, executive dysfunction, and personality changes.
TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature Scaling
Furthermore, the MC-Model module incorporates full-sampling k-space information, realizing efficient fusion of conditional information, enhancing the model's ability to process complex data, and improving the realism and detail richness of reconstructed images.
GFE-Mamba: Mamba-based AD Multi-modal Progression Assessment via Generative Feature Extraction from MCI
Alzheimer's Disease (AD) is an irreversible neurodegenerative disorder that often progresses from Mild Cognitive Impairment (MCI), leading to memory loss and significantly impacting patients' lives.
SCKansformer: Fine-Grained Classification of Bone Marrow Cells via Kansformer Backbone and Hierarchical Attention Mechanisms
The model integrates the Kansformer Encoder, SCConv Encoder, and Global-Local Attention Encoder.
SRGS: Super-Resolution 3D Gaussian Splatting
This approach relies on the representation power of Gaussian primitives to provide a high-quality rendering.
CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model
In this work, we present the Convolutional Reconstruction Model (CRM), a high-fidelity feed-forward single image-to-3D generative model.
Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction
It is designed to enhance the model's generalizability by leveraging the interaction between localized visual patterns and fine-grained pathological semantics.
TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet method
We also suggest the incorporation of the MF-UNet module, designed to enhance the quality of MRI images generated by the model while mitigating the over-smoothing issue to a certain extent.
Semi-supervised Medical Image Segmentation Method Based on Cross-pseudo Labeling Leveraging Strong and Weak Data Augmentation Strategies
Traditional supervised learning methods have historically encountered certain constraints in medical image segmentation due to the challenging collection process, high labeling cost, low signal-to-noise ratio, and complex features characterizing biomedical images.
A Survey on Hallucination in Large Vision-Language Models
In this comprehensive survey, we dissect LVLM-related hallucinations in an attempt to establish an overview and facilitate future mitigation.
INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning
Despite this, their application to information retrieval (IR) tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language.
Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases
To address these issues, this paper proposes an innovative method of leukocyte detection: the Multi-level Feature Fusion and Deformable Self-attention DETR (MFDS-DETR).
SCUNet++: Swin-UNet and CNN Bottleneck Hybrid Architecture with Multi-Fusion Dense Skip Connection for Pulmonary Embolism CT Image Segmentation
These findings demonstrate that our method exhibits strong performance in PE segmentation tasks, potentially enhancing the accuracy of automatic segmentation of PE and providing a powerful diagnostic tool for clinical physicians.
Align before Adapt: Leveraging Entity-to-Region Alignments for Generalizable Video Action Recognition
With the aligned entities, we feed their text embeddings to a transformer-based video adapter as the queries, which can help extract the semantics of the most important entities from a video to a vector.
Warfare:Breaking the Watermark Protection of AI-Generated Content
We propose Warfare, a unified methodology to achieve both attacks in a holistic way.
Unveiling the Hidden Realm: Self-supervised Skeleton-based Action Recognition in Occluded Environments
To integrate action recognition methods into autonomous robotic systems, it is crucial to consider adverse situations involving target occlusions.
ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection
Effective feature fusion of multispectral images plays a crucial role in multi-spectral object detection.
Ranked #2 on
Object Detection
on VEDAI
ChartDETR: A Multi-shape Detection Network for Visual Chart Recognition
Visual chart recognition systems are gaining increasing attention due to the growing demand for automatically identifying table headers and values from chart images.
Efficient information recovery from Pauli noise via classical shadow
The rapid advancement of quantum computing has led to an extensive demand for effective techniques to extract classical information from quantum systems, particularly in fields like quantum machine learning and quantum chemistry.
Video Action Recognition with Attentive Semantic Units
Supervised by the semantics of action labels, recent works adapt the visual branch of VLMs to learn video representations.
PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation
In this paper, we propose the token-Pruned Pose Transformer (PPT) for 2D human pose estimation, which can locate a rough human mask and performs self-attention only within selected tokens.
Ranked #17 on
3D Human Pose Estimation
on Human3.6M
(using extra training data)
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services.
Rotation-invariant Mixed Graphical Model Network for 2D Hand Pose Estimation
In this paper, we propose a new architecture named Rotation-invariant Mixed Graphical Model Network (R-MGMN) to solve the problem of 2D hand pose estimation from a monocular RGB image.
Nonparametric Structure Regularization Machine for 2D Hand Pose Estimation
We propose a novel Nonparametric Structure Regularization Machine (NSRM) for 2D hand pose estimation, adopting a cascade multi-task architecture to learn hand structure and keypoint representations jointly.
Adaptive Graphical Model Network for 2D Handpose Estimation
In this paper, we propose a new architecture called Adaptive Graphical Model Network (AGMN) to tackle the task of 2D hand pose estimation from a monocular RGB image.
Inference in Kingman's Coalescent with Particle Markov Chain Monte Carlo Method
We propose a new algorithm to do posterior sampling of Kingman's coalescent, based upon the Particle Markov Chain Monte Carlo methodology.
