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Found 37 papers, 20 papers with code
Diff-VPS: Video Polyp Segmentation via a Multi-task Diffusion Network with Adversarial Temporal Reasoning
We incorporate multi-task supervision into diffusion models to promote the discrimination of diffusion models on pixel-by-pixel segmentation.
Timeline and Boundary Guided Diffusion Network for Video Shadow Detection
Moreover, we are the first to introduce the Diffusion model for VSD in which we explore a Space-Time Encoded Embedding (STEE) to inject the temporal guidance for Diffusion to conduct shadow detection.
Towards Non-invasive and Personalized Management of Breast Cancer Patients from Multiparametric MRI via A Large Mixture-of-Modality-Experts Model
MOME exemplifies a discriminative, robust, scalable, and interpretable multimodal model, paving the way for noninvasive, personalized management of breast cancer patients based on multiparametric breast imaging data.
Surgformer: Surgical Transformer with Hierarchical Temporal Attention for Surgical Phase Recognition
Moreover, we propose a novel Hierarchical Temporal Attention (HTA) to capture both global and local information within varied temporal resolutions from a target frame-centric perspective.
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset
In response, this study introduces a pioneering framework that leverages a patch-based active domain adaptation approach.
Ultrasound Report Generation with Cross-Modality Feature Alignment via Unsupervised Guidance
In this work, we propose a novel framework for automatic ultrasound report generation, leveraging a combination of unsupervised and supervised learning methods to aid the report generation process.
MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis
We further scale up the MiM to large pre-training datasets with more than 10k volumes, showing that large-scale pre-training can further enhance the performance of downstream tasks.
Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-training
We leverage descriptions in medical reports to design multi-granular question-answer pairs associated with different diseases, which assist the framework in pre-training without requiring extra annotations from experts.
Progressive Frequency-Aware Network for Laparoscopic Image Desmoking
Laparoscopic surgery offers minimally invasive procedures with better patient outcomes, but smoke presence challenges visibility and safety.
Shifting More Attention to Breast Lesion Segmentation in Ultrasound Videos
We also devise a localization-based contrastive loss to reduce the lesion location distance between neighboring video frames within the same video and enlarge the location distances between frames from different ultrasound videos.
Ranked #3 on
Video Polyp Segmentation
on SUN-SEG-Easy
Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes
Unfortunately, the resultant noisy bounding boxes could cause corrupt supervision signals and thus diminish detection performance.
Semi-Supervised Semantic Segmentation With Region Relevance
The most common approach is to generate pseudo-labels for unlabeled images to augment the training data.
Learning Robust Medical Image Segmentation from Multi-source Annotations
Learning segmentation networks from multi-source annotations remains a challenge due to the uncertainties brought by the variance of annotations and the quality of images.
Masked Image Training for Generalizable Deep Image Denoising
To address this issue, we present a novel approach to enhance the generalization performance of denoising networks, known as masked training.
FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network
Few-shot semantic segmentation is the task of learning to locate each pixel of the novel class in the query image with only a few annotated support images.
Ranked #2 on
Few-Shot Semantic Segmentation
on COCO-20i (10-shot)
RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction
In biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures.
Intra-Modal Constraint Loss For Image-Text Retrieval
Cross-modal retrieval has drawn much attention in both computer vision and natural language processing domains.
Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation
Specifically, we propose a saliency guided class-agnostic distance module to pull the intra-category features closer by aligning features to their class prototypes.
Acknowledging the Unknown for Multi-label Learning with Single Positive Labels
Due to the difficulty of collecting exhaustive multi-label annotations, multi-label datasets often contain partial labels.
GeoBi-GNN: Geometry-aware Bi-domain Mesh Denoising via Graph Neural Networks
For the first time, we optimize both positions and normals (i. e., dual domains) in a unified framework of GNN, and show the powerful inter-coordination between the dual domains.
PNP: Robust Learning From Noisy Labels by Probabilistic Noise Prediction
Label noise has been a practical challenge in deep learning due to the strong capability of deep neural networks in fitting all training data.
Hepatic vessel segmentation based on 3D swin-transformer with inductive biased multi-head self-attention
In practice, we introduce the voxel-wise embedding rather than patch-wise embedding to locate precise liver vessel voxels, and adopt multi-scale convolutional operators to gain local spatial information.
Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning
The backpropagation networks are notably susceptible to catastrophic forgetting, where networks tend to forget previously learned skills upon learning new ones.
Ammonia-induced Calcium Phosphate Nanostructure: A Potential Assay for Studying Osteoporosis and Bone Metastasis
To demon-strate the application in studying bone metastasis, we delivered PC3 prostate cancer conditioned medium and confirmed that both the differentiation of monocytes into osteoclasts and the osteoclastic resorption of the calcium phosphate coating were significantly enhanced.
Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation
Then we utilize the fused prototype to guide the final segmentation of the query image.
Globular structure of the hypermineralized tissue in human femoral neck
Transmission electron microscopy showed the apatite inside globules were poorly crystalline, while those at the boundaries between the globules had well-defined lattice structure with crystallinity similar to the apatite mineral in lamellar bone.
Instance Shadow Detection
Then, we pair up the predicted shadow and object instances, and match them with the predicted shadow-object associations to generate the final results.
Ranked #3 on
Instance Shadow Detection
on SOBA
Revisiting Shadow Detection: A New Benchmark Dataset for Complex World
Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world.
Ranked #10 on
Shadow Detection
on CUHK-Shadow
Group Sparsity Residual with Non-Local Samples for Image Denoising
Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing.
Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering
Spectral Clustering is one of pioneered clustering methods in machine learning and pattern recognition field.
Group-based Sparse Representation for Image Compressive Sensing Reconstruction with Non-Convex Regularization
In this paper, a group-based sparse representation method with non-convex regularization (GSR-NCR) for image CS reconstruction is proposed.
Non-Convex Weighted Lp Nuclear Norm based ADMM Framework for Image Restoration
Since the matrix formed by nonlocal similar patches in a natural image is of low rank, the nuclear norm minimization (NNM) has been widely used in various image processing studies.
Non-Convex Weighted Lp Minimization based Group Sparse Representation Framework for Image Denoising
Nonlocal image representation or group sparsity has attracted considerable interest in various low-level vision tasks and has led to several state-of-the-art image denoising techniques, such as BM3D, LSSC.
Group Sparsity Residual Constraint for Image Denoising
Unlike the conventional group-based sparse representation denoising methods, two kinds of prior, namely, the NSS priors of noisy and pre-filtered images, are used in GSRC.
Image denoising using group sparsity residual and external nonlocal self-similarity prior
To boost the performance of image denoising, the concept of group sparsity residual is proposed, and thus the problem of image denoising is transformed into one that reduces the group sparsity residual.
Analyzing the group sparsity based on the rank minimization methods
Then, we prove that group-based sparse coding is equivalent to the rank minimization problem, and thus the sparse coefficient of each group is measured by estimating the singular values of each group.
Image denoising via group sparsity residual constraint
Group sparsity has shown great potential in various low-level vision tasks (e. g, image denoising, deblurring and inpainting).
