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Found 43 papers, 34 papers with code
Intra-class Feature Variation Distillation for Semantic Segmentation
In this paper, different from previous methods performing knowledge distillation for densely pairwise relations, we propose a novel intra-class feature variation distillation (IFVD) to transfer the intra-class feature variation (IFV) of the cumbersome model (teacher) to the compact model (student).
Rejoining fragmented ancient bamboo slips with physics-driven deep learning
This approach enables the training of a matching network without requiring manually paired samples, providing ranked suggestions to facilitate the rejoining process.
LiftFeat: 3D Geometry-Aware Local Feature Matching
We then design a 3D geometry-aware feature lifting module to fuse surface normal feature with raw 2D descriptor feature.
CQ-DINO: Mitigating Gradient Dilution via Category Queries for Vast Vocabulary Object Detection
To address these challenges, we propose CQ-DINO, a category query-based object detection framework that reformulates classification as a contrastive task between object queries and learnable category queries.
Consistent-Point: Consistent Pseudo-Points for Semi-Supervised Crowd Counting and Localization
Crowd counting and localization are important in applications such as public security and traffic management.
Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide Image Classification
Secondly, it trains separate classification heads for each task and uses macroscopic pathological prior knowledge, treating the thumbnail as a prompt guide (PG) to select the appropriate classification head.
RefDrone: A Challenging Benchmark for Referring Expression Comprehension in Drone Scenes
NGDINO explicitly learns and utilizes the number of objects referred to in the expression.
Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation
These encoded priors are injected into the segmentation model as parameters of the segmentation head.
MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching
Extending such methods to multimodal image matching often requires well-aligned multimodal data to learn modality-invariant descriptors.
CellSeg1: Robust Cell Segmentation with One Training Image
We introduce CellSeg1, a practical solution for segmenting cells of arbitrary morphology and modality with a few dozen cell annotations in 1 image.
Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image Segmentation
The significant size differences among various organs in the human body lead to imbalanced class distribution, which is a major challenge in the real-world application of these SSL approaches.
Shape-intensity knowledge distillation for robust medical image segmentation
In this paper, we propose a novel approach to incorporate joint shape-intensity prior information into the segmentation network.
Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation
In this paper, we propose a novel Domain-Adaptive Prompt framework for fine-tuning the Segment Anything Model (termed as DAPSAM) to address single-source domain generalization (SDG) in segmenting medical images.
Progressive Retinal Image Registration via Global and Local Deformable Transformations
For that, we use a keypoint detector and a deformation network called GAMorph to estimate the global transformation and local deformable transformation, respectively.
HFGS: 4D Gaussian Splatting with Emphasis on Spatial and Temporal High-Frequency Components for Endoscopic Scene Reconstruction
Robot-assisted minimally invasive surgery benefits from enhancing dynamic scene reconstruction, as it improves surgical outcomes.
Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical Image
Medical anomaly detection is a critical research area aimed at recognizing abnormal images to aid in diagnosis. Most existing methods adopt synthetic anomalies and image restoration on normal samples to detect anomaly.
Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays
Anomaly detection in chest X-rays is a critical task.
Development of Skip Connection in Deep Neural Networks for Computer Vision and Medical Image Analysis: A Survey
Deep learning has made significant progress in computer vision, specifically in image classification, object detection, and semantic segmentation.
MoreStyle: Relax Low-frequency Constraint of Fourier-based Image Reconstruction in Generalizable Medical Image Segmentation
The task of single-source domain generalization (SDG) in medical image segmentation is crucial due to frequent domain shifts in clinical image datasets.
WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising
Second, to better capture high-frequency components and detailed information, Frequency-Aware Multi-scale Loss (FAM) is proposed by effectively utilizing multi-scale feature space.
Shifted Autoencoders for Point Annotation Restoration in Object Counting
Object counting typically uses 2D point annotations.
Dual Structure-Aware Image Filterings for Semi-supervised Medical Image Segmentation
In this paper, we propose novel dual structure-aware image filterings (DSAIF) as the image-level variations for semi-supervised medical image segmentation.
Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation
Extensive experiments on both open-source and in-house datasets consistently demonstrate the effectiveness of the proposed method over some CNN and Transformer-based segmentation methods.
Not All Pixels Are Equal: Learning Pixel Hardness for Semantic Segmentation
Yet, most existing hard pixel mining strategies for semantic segmentation often rely on pixel's loss value, which tends to decrease during training.
Scratch Each Other's Back: Incomplete Multi-Modal Brain Tumor Segmentation via Category Aware Group Self-Support Learning
In this paper, considering the sensitivity of different modalities to diverse tumor regions, we propose a Category Aware Group Self-Support Learning framework, called GSS, to make up for the information deficit among the modalities in the individual modal feature extraction phase.
Local Intensity Order Transformation for Robust Curvilinear Object Segmentation
This results in a representation that preserves the inherent characteristic of the curvilinear structure while being robust to contrast changes.
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.
Affinity Space Adaptation for Semantic Segmentation Across Domains
Specifically, we perform domain adaptation on the affinity relationship between adjacent pixels termed affinity space of source and target domain.
Learning Directional Feature Maps for Cardiac MRI Segmentation
Cardiac MRI segmentation plays a crucial role in clinical diagnosis for evaluating personalized cardiac performance parameters.
Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation
In this paper, we propose a fast image segmentation method based on a novel super boundary-to-pixel direction (super-BPD) and a customized segmentation algorithm with super-BPD.
AutoSTR: Efficient Backbone Search for Scene Text Recognition
In this work, inspired by the success of neural architecture search (NAS), which can identify better architectures than human-designed ones, we propose automated STR (AutoSTR) to search data-dependent backbones to boost text recognition performance.
AutoScale: Learning to Scale for Crowd Counting and Localization
A major issue is that the density map on dense regions usually accumulates density values from a number of nearby Gaussian blobs, yielding different large density values on a small set of pixels.
All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting
Recently, end-to-end text spotting that aims to detect and recognize text from cluttered images simultaneously has received particularly growing interest in computer vision.
Gliding vertex on the horizontal bounding box for multi-oriented object detection
Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts.
Ranked #48 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting
Dense crowd counting aims to predict thousands of human instances from an image, by calculating integrals of a density map over image pixels.
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Segmentation methods had to be containerized and submitted to the challenge organizers.
TextField: Learning A Deep Direction Field for Irregular Scene Text Detection
Experimental results show that the proposed TextField outperforms the state-of-the-art methods by a large margin (28% and 8%) on two curved text datasets: Total-Text and CTW1500, respectively, and also achieves very competitive performance on multi-oriented datasets: ICDAR 2015 and MSRA-TD500.
Ranked #22 on
Scene Text Detection
on Total-Text
DeepFlux for Skeletons in the Wild
In the present article, we depart from this strategy by training a CNN to predict a two-dimensional vector field, which maps each scene point to a candidate skeleton pixel, in the spirit of flux-based skeletonization algorithms.
Ranked #1 on
Object Skeleton Detection
on SK-LARGE
Hard-Aware Point-to-Set Deep Metric for Person Re-identification
Person re-identification (re-ID) is a highly challenging task due to large variations of pose, viewpoint, illumination, and occlusion.
Deep-Person: Learning Discriminative Deep Features for Person Re-Identification
Recently, many methods of person re-identification (Re-ID) rely on part-based feature representation to learn a discriminative pedestrian descriptor.
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification
Then, we combine the word embedding of the recognized words and the deep visual features into a single representation, which is optimized by a convolutional neural network for fine-grained image classification.
Hierarchical image simplification and segmentation based on Mumford-Shah-salient level line selection
Many image simplification and segmentation methods are driven by the optimization of an energy functional, for instance the celebrated Mumford-Shah functional.
