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Chapter and Conference Paper
A Foreground Feature Embedding Network for Object Detection in Remote Sensing Images
Compared with traditional natural images, remote sensing images (RSIs) typically have high resolution. The objects in the images are densely distributed, with heterogeneous orientation and large scale variatio...
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Chapter and Conference Paper
TPNet: Enhancing Weakly Supervised Polyp Frame Detection with Temporal Encoder and Prototype-Based Memory Bank
Polyp detection plays a crucial role in the early prevention of colorectal cancer. The availability of large-scale polyp video datasets and video-level annotations has spurred research efforts to formulate pol...
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Chapter and Conference Paper
A Multi-stage Network with Self-attention for Tooth Instance Segmentation
Automatic and accurate instance segmentation of teeth from 3D Cone-Beam Computer Tomography (CBCT) images is crucial for dental diagnose. Although Convolutional Neural Networks (CNNs) are widely used for tooth...
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Article
Open AccessDigital light processing 3D printing for microfluidic chips with enhanced resolution via dosing- and zoning-controlled vat photopolymerization
Conventional manufacturing techniques to fabricate microfluidic chips, such as soft lithography and hot embossing process, have limitations that include difficulty in preparing multiple-layered structures, cos...
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Chapter and Conference Paper
A Classifier-Based Two-Stage Training Model for Few-Shot Segmentation
Over the past few years, deep learning-based semantic segmentation methods reached state-of-the-art performance. The segmentation task is time-consuming and requires a lot of pixel-level annotated data, which ...
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Chapter and Conference Paper
Detect Any Deepfakes: Segment Anything Meets Face Forgery Detection and Localization
The rapid advancements in computer vision have stimulated remarkable progress in face forgery techniques, capturing the dedicated attention of researchers committed to detecting forgeries and precisely localiz...
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Chapter and Conference Paper
A Improved Prior Box Generation Method for Small Object Detection
As a task in object detection, small object detection mainly focuses on detecting objects of small size, which is more complex than general object detection. It is pivotal in various applications, e.g., small ...
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Chapter and Conference Paper
Boundary Difference over Union Loss for Medical Image Segmentation
Medical image segmentation is crucial for clinical diagnosis. However, current losses for medical image segmentation mainly focus on overall segmentation results, with fewer losses proposed to guide boundary s...
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Chapter and Conference Paper
Generalized Person Re-identification by Locating and Eliminating Domain-Sensitive Features
In this paper, we study the problem of domain generalization for person re-identification (re-ID), which adopts training data from multiple domains to learn a re-ID model that can be directly deployed to unsee...
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Article
Improving embedding learning by virtual attribute decoupling for text-based person search
This paper considers the problem of text-based person search, which aims to find the target person based on a query textual description. Previous methods commonly focus on learning shared image-text embeddings...
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Chapter and Conference Paper
Attention and Multi-granied Feature Learning for Baggage Re-identification
The current baggage re-identification methods only consider the global coarse-grained features while ignoring the fine-grained features. To deal with this issue, we proposed a simple and efficient multi-granul...
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Chapter and Conference Paper
An Improved SSD-Based Gastric Cancer Detection Method
Gastric cancer is one of the malignant cancers with a very high fatal rate, and early detection plays an essential role in the treatment and improves the five-year 5-year survival rate. In this study, we an im...
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Chapter and Conference Paper
A Semi-supervised Video Object Segmentation Method Based on Adaptive Memory Module
Video object segmentation has becoming a hot research topic in the computer vision society, with a wide range of applications, such as autonomous driving, video editing, and video surveillance. However, due to...
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Article
SAFD: single shot anchor free face detector
The anchor-free based face detection methods can cover a large range of scales and perform better in the speed. However, their performance still bears a large gap compared with anchor-based methods, especially...
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Chapter and Conference Paper
Learning Consistency- and Discrepancy-Context for 2D Organ Segmentation
Recently, CNN-based methods lead tremendous progress in segmenting abdominal organs (e.g., kidney, liver, and pancreas) and anomaly tumors in CT scans. Although 3D CNN-based methods can significantly improve accu...
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Chapter and Conference Paper
Law Article Prediction via a Codex Enhanced Multi-task Learning Framework
Automatic law article prediction aims to determine appropriate laws for a case by analyzing its corresponding fact description. This research constitutes a relatively new area which has emerged from recommende...
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Article
Screening of Polymorphic SSR Molecular Markers Between Resistant and Susceptible Parents for Localization of Brown Rust Resistance Gene
Sugarcane brown rust induced by Puccinia melanocephala is an important global disease. Exploring novel resistance genes and breeding varieties with durable resistance is the most economical and effective way of c...
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Article
Multimodal Information Fusion for Automatic Aesthetics Evaluation of Robotic Dance Poses
Aesthetic ability is an advanced cognitive function of human beings. Human dancers in front of mirrors estimate the aesthetics of their own dance poses by fusing multimodal information (visual and non-visual) ...
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Chapter and Conference Paper
A Fast Feature Selection Method Based on Mutual Information in Multi-label Learning
Recently, multi-label learning is concerned and studied in lots of fields by many researchers. However, multi-label datasets often have noisy, irrelevant and redundant features with high dimensionality. Accomp...
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Chapter and Conference Paper
A Simple and Convex Formulation for Multi-label Feature Selection
In recent years, multi-label study has received extensive attention and research in many fields. The feature dimensions of a multi-label data set are high but contain a large amount of noise as well as irrelev...
