Search Results for author:
Found 19 papers, 7 papers with code
Rethinking Person Re-Identification via Semantic-Based Pretraining
Pretraining is a dominant paradigm in computer vision.
Adaptive Context Selection for Polyp Segmentation
To tackle these issues, we propose an adaptive context selection based encoder-decoder framework which is composed of Local Context Attention (LCA) module, Global Context Module (GCM) and Adaptive Selection Module (ASM).
Learning-Based Framework for Camera Calibration with Distortion Correction and High Precision Feature Detection
Therefore, in this paper, we propose a hybrid camera calibration framework which combines learning-based approaches with traditional methods to handle these bottlenecks.
A simple normalization technique using window statistics to improve the out-of-distribution generalization on medical images
In this study, we present a novel normalization technique called window normalization (WIN) to improve the model generalization on heterogeneous medical images, which is a simple yet effective alternative to existing normalization methods.
Deep Multimodal Fusion for Generalizable Person Re-identification
Person re-identification plays a significant role in realistic scenarios due to its various applications in public security and video surveillance.
Domain Generalization
Generalizable Person Re-identification
+2
Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification
In this paper, we propose a Multi-Modal Equivalent Transformer called MMET for more robust visual-semantic embedding learning on visual, textual and visual-textual tasks respectively.
Generalizable Person Re-identification
Representation Learning
Learning Discriminative Visual-Text Representation for Polyp Re-Identification
Colonoscopic Polyp Re-Identification aims to match a specific polyp in a large gallery with different cameras and views, which plays a key role for the prevention and treatment of colorectal cancer in the computer-aided diagnosis.
Multi-Lead ECG Classification via an Information-Based Attention Convolutional Neural Network
Models embedded with the channel-wise attention structure always achieve better scores on sensitivity and precision than the plain Resnet models.
An End to End Network Architecture for Fundamental Matrix Estimation
In this paper, we present a novel end-to-end network architecture to estimate fundamental matrix directly from stereo images.
Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images
Specifically, the encoder of a DL model that is pre-trained on the source domain is used to initialize the encoder of a reconstruction model.
Real-time automatic polyp detection in colonoscopy using feature enhancement module and spatiotemporal similarity correlation unit
In order to solve these problems, our method combines the two-dimensional (2-D) CNN-based real-time object detector network with spatiotemporal information.
SubFace: Learning with Softmax Approximation for Face Recognition
The softmax-based loss functions and its variants (e. g., cosface, sphereface, and arcface) significantly improve the face recognition performance in wild unconstrained scenes.
FusePose: IMU-Vision Sensor Fusion in Kinematic Space for Parametric Human Pose Estimation
On Total Capture dataset, KineFuse surpasses previous state-of-the-art which uses IMU only for testing by 8. 6\%.
Ranked #2 on
3D Human Pose Estimation
on Total Capture
Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval
To seek a solution to this problem, we construct a large-scale colonoscopic dataset named Colo-Pair for medical practice.
Searching from Area to Point: A Hierarchical Framework for Semantic-Geometric Combined Feature Matching
This paper, thus, pays attention to the search space and proposes to set the initial search space for point matching as the matched image areas containing prominent semantic, named semantic area matches.
Towards Discriminative Representation with Meta-learning for Colonoscopic Polyp Re-Identification
Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras and plays an important role in the prevention and treatment of colorectal cancer in computer-aided diagnosis.
Towards Open-set Gesture Recognition via Feature Activation Enhancement and Orthogonal Prototype Learning
While there has been significant progress in gesture recognition based on surface electromyography (sEMG), accurate recognition of predefined gestures only within a closed set is still inadequate in practice.
Supervised Contrastive Learning for Fine-grained Chromosome Recognition
Chromosome recognition is an essential task in karyotyping, which plays a vital role in birth defect diagnosis and biomedical research.
Skeleton-Guided Instance Separation for Fine-Grained Segmentation in Microscopy
One of the fundamental challenges in microscopy (MS) image analysis is instance segmentation (IS), particularly when segmenting cluster regions where multiple objects of varying sizes and shapes may be connected or even overlapped in arbitrary orientations.
