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Found 16 papers, 2 papers with code
Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances
In this era of rapid technical evolution, this review aims to present a comprehensive review of the recent achievements in deep learning based RSOD methods.
DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model
In this work, we extend the diffusion model's application to the HSI-CD field and propose a novel unsupervised HSI-CD with semantic correlation diffusion model (DiffUCD).
SoftMatch Distance: A Novel Distance for Weakly-Supervised Trend Change Detection in Bi-Temporal Images
Therefore, there is a novel solution that intuitively dividing changes into three trends (``appear'', ``disappear'' and ``transform'') instead of semantic categories, named it trend change detection (TCD) in this paper.
Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information
In the exploiting stage, we utilize the extracted NDI to construct a novel negative contrastive learning mechanism and a negative guided instance selection strategy for dealing with the issues of part domination and missing instances, respectively.
MidNet: An Anchor-and-Angle-Free Detector for Oriented Ship Detection in Aerial Images
Ship detection in aerial images remains an active yet challenging task due to arbitrary object orientation and complex background from a bird's-eye perspective.
Adaptive Affinity Loss and Erroneous Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation
Semantic segmentation has been continuously investigated in the last ten years, and majority of the established technologies are based on supervised models.
Unsupervised Outlier Detection using Memory and Contrastive Learning
Instead, we think outlier detection can be done in the feature space by measuring the feature distance between outliers and inliers.
Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images
To address the above problems, we propose an end-to-end multi-category instance segmentation model, namely Semantic Attention and Scale Complementary Network, which mainly consists of a Semantic Attention (SEA) module and a Scale Complementary Mask Branch (SCMB).
Deep Learning Based Brain Tumor Segmentation: A Survey
Brain tumor segmentation is one of the most challenging problems in medical image analysis.
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification
To address this problem, a symmetric convolutional GAN based on collaborative learning and attention mechanism (CA-GAN) is proposed.
Ranked #7 on
Hyperspectral Image Classification
on Indian Pines
Few-Shot Image Classification
Generative Adversarial Network
+2
OpenHI2 -- Open source histopathological image platform
Transition from conventional to digital pathology requires a new category of biomedical informatic infrastructure which could facilitate delicate pathological routine.
Effects of annotation granularity in deep learning models for histopathological images
In classification, state-of-the-art deep learning-based classifiers perform better when trained by pixel-wise annotation dataset.
Detection and Tracking of Multiple Mice Using Part Proposal Networks
The study of mouse social behaviours has been increasingly undertaken in neuroscience research.
Cost-sensitive Boosting Pruning Trees for depression detection on Twitter
Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year.
