8 Result(s)
within
Zhiming Luo
Computer Applications
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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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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...
