Search Results for author:
Found 23 papers, 9 papers with code
Enhanced Masked Image Modeling for Analysis of Dental Panoramic Radiographs
Another barrier in dental imaging is the limited number of available images for training, which is a challenge in the era of deep learning.
DMDD: A Large-Scale Dataset for Dataset Mentions Detection
We use DMDD to establish baseline performance for dataset mention detection and linking.
Graph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classification
In spite of many advances, most of the approaches require a large amount of labeled data, which is often not available, due to costs and difficulties of manual labeling processes.
Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning
High effective results demonstrate the effectiveness of the proposed method on different tasks: unsupervised image retrieval, semi-supervised classification, and person Re-ID.
Weakly Supervised Annotations for Multi-modal Greeting Cards Dataset
In our work, we propose to aggregate features from pretrained images and text embeddings to learn abstract visual concepts from GCD.
Self-Supervised Learning with Masked Image Modeling for Teeth Numbering, Detection of Dental Restorations, and Instance Segmentation in Dental Panoramic Radiographs
To the best of our knowledge, this is the first study that applied self-supervised learning methods to Swin Transformer on dental panoramic radiographs.
DPNet: Dual-Path Network for Real-time Object Detection with Lightweight Attention
The recent advances of compressing high-accuracy convolution neural networks (CNNs) have witnessed remarkable progress for real-time object detection.
Ranked #12 on
Object Detection
on PASCAL VOC 2007
DRBANET: A Lightweight Dual-Resolution Network for Semantic Segmentation with Boundary Auxiliary
Due to the powerful ability to encode image details and semantics, many lightweight dual-resolution networks have been proposed in recent years.
DPNET: Dual-Path Network for Efficient Object Detectioj with Lightweight Self-Attention
Object detection often costs a considerable amount of computation to get satisfied performance, which is unfriendly to be deployed in edge devices.
Leveraging Line-Point Consistence To Preserve Structures for Wide Parallax Image Stitching
Generating high-quality stitched images with natural structures is a challenging task in computer vision.
Pushing the Envelope of Rotation Averaging for Visual SLAM
As an essential part of structure from motion (SfM) and Simultaneous Localization and Mapping (SLAM) systems, motion averaging has been extensively studied in the past years and continues to attract surging research attention.
BiCANet: Bi-directional Contextual Aggregating Network for Image Semantic Segmentation
Exploring contextual information in convolution neural networks (CNNs) has gained substantial attention in recent years for semantic segmentation.
Entropy Minimization vs. Diversity Maximization for Domain Adaptation
Entropy minimization has been widely used in unsupervised domain adaptation (UDA).
Ranked #6 on
Domain Adaptation
on ImageCLEF-DA
Dually Supervised Feature Pyramid for Object Detection and Segmentation
Feature pyramid architecture has been broadly adopted in object detection and segmentation to deal with multi-scale problem.
LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation
LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation
Ranked #29 on
Real-Time Semantic Segmentation
on Cityscapes test
Scene Parsing via Dense Recurrent Neural Networks with Attentional Selection
Recurrent neural networks (RNNs) have shown the ability to improve scene parsing through capturing long-range dependencies among image units.
Advancing Tabu and Restart in Local Search for Maximum Weight Cliques
Our new restart strategy is based on the re-occurrence of a local search scenario instead of that of a candidate solution.
Ensemble Diffusion for Retrieval
This stimulates a great research interest of considering similarity fusion in the framework of diffusion process (i. e., fusion with diffusion) for robust retrieval.
Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes From 2D Ones in RGB-Depth Images
We revisit the amodal 3D detection problem by sticking to the 2. 5D representation framework, and directly relate 2. 5D visual appearance to 3D objects.
Multidimensional Scaling on Multiple Input Distance Matrices
How to do multidimensional scaling on multiple input distance matrices is still unsolved to our best knowledge.
GIFT: A Real-time and Scalable 3D Shape Search Engine
We name the proposed 3D shape search engine, which combines GPU acceleration and Inverted File Twice, as GIFT.
Semantic Segmentation of RGBD Images With Mutex Constraints
In this paper, we address the problem of semantic scene segmentation of RGB-D images of indoor scenes.
Graph Transduction Learning with Connectivity Constraints with Application to Multiple Foreground Cosegmentation
The proposed approach is based on standard graph transduction, semi-supervised learning (SSL) framework.
