Search Results for author:
Found 22 papers, 18 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).
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.
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 #38 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
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.
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.
