227 Result(s)
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Article
Single-Temporal Supervised Learning for Universal Remote Sensing Change Detection
Bitemporal supervised learning paradigm always dominates remote sensing change detection using numerous labeled bitemporal image pairs, especially for high spatial resolution (HSR) remote sensing imagery. Howe...
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Article
SSE-YOLOv5: a real-time fault line selection method based on lightweight modules and attention models
To address the problems of low precision and poor anti-noise performance of the standard route selection method for the small current grounding faults, a fault line selection approach based on YOLOv5 network t...
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Article
Correction to: FBRNet: a feature fusion and border refinement network for real-time semantic segmentation
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Article
FSODv2: A Deep Calibrated Few-Shot Object Detection Network
Traditional methods for object detection typically necessitate a substantial amount of training data, and creating high-quality training data is time-consuming. We propose a novel Few-Shot Object Detection net...
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Article
FBRNet: a feature fusion and border refinement network for real-time semantic segmentation
Existing semantic segmentation networks perform well in accuracy by spending much computation. However, for practical applications, not only high segmentation accuracy but also high inference speed is required...
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Chapter and Conference Paper
All You See Is the Tip of the Iceberg: Distilling Latent Interactions Can Help You Find Treasures
Recommender systems suffer from data sparsity problem severely, which can be attributed to the combined action of various possible causes like: gradually strengthened privacy protection policies, exposure bias...
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Chapter and Conference Paper
TPTGAN: Two-Path Transformer-Based Generative Adversarial Network Using Joint Magnitude Masking and Complex Spectral Map** for Speech Enhancement
In recent studies, conformer is extensively employed in speech enhancement. Nevertheless, it continues to confront the challenge of excessive suppression, especially in human-to-machine communication, attribut...
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Chapter and Conference Paper
Self-adaptive Inverse Soft-Q Learning for Imitation
As a powerful method for solving sequential decision problems, imitation learning (IL) aims to generate policy similar to expert behavior by imitating demonstrations. However, the quality of demonstrations dir...
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Chapter and Conference Paper
Visible and NIR Image Fusion Algorithm Based on Information Complementarity
Visible and near-infrared (NIR) band sensors provide images that capture complementary spectral radiations from a scene. And the fusion of the visible and NIR image aims at utilizing their spectrum properties ...
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Chapter and Conference Paper
Dual-Domain Network for Restoring Images from Under-Display Cameras
With the increasing popularity of full-screen devices, phone manufacturers have started placing cameras behind screens to increase the percentage of the displays. However, this innovative approach, known as un...
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Chapter and Conference Paper
CAS-NN: A Robust Cascade Neural Network Without Compromising Clean Accuracy
Adversarial training has emerged as a prominent approach for training robust classifiers. However, recent researches indicate that adversarial training inevitably results in a decline in a classifier’s accurac...
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Chapter and Conference Paper
ASRCD: Adaptive Serial Relation-Based Model for Cognitive Diagnosis
Cognitive diagnosis (CD) is a critical task in the education field, aimed at assessing the true concept proficiency of learners. Recent studies have highlighted the significance of concept relations (e.g., co...
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Chapter and Conference Paper
“Car or Bus?" CLearSeg: CLIP-Enhanced Discrimination Among Resembling Classes for Few-Shot Semantic Segmentation
Few-shot semantic segmentation aims at learning to segment query images of unseen classes with the guidance of limited segmented support examples. However, existing models tend to confuse the resembling classes (
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Chapter and Conference Paper
Progressive Temporal Transformer for Bird’s-Eye-View Camera Pose Estimation
Visual relocalization is a crucial technique used in visual odometry and SLAM to predict the 6-DoF camera pose of a query image. Existing works mainly focus on ground view in indoor or outdoor scenes. However,...
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Article
Exploring Vision-Language Models for Imbalanced Learning
Vision-language models (VLMs) that use contrastive language-image pre-training have shown promising zero-shot classification performance. However, their performance on imbalanced dataset is relatively poor, wh...
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Article
DCP–NAS: Discrepant Child–Parent Neural Architecture Search for 1-bit CNNs
Neural architecture search (NAS) proves to be among the effective approaches for many tasks by generating an application-adaptive neural architecture, which is still challenged by high computational cost and m...
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Chapter and Conference Paper
UAU-Net: United Attention U-Shaped Network for the Segmentation of Pigment Deposits in Fundus Images of Retinitis Pigmentosa
Retinitis Pigmentosa (RP) is a retinal disease with high rate of blindness. Retinal pigment deposits are a typical symptom of RP, whose automatic segmentation is crucial to the early diagnosis of RP. In fundus...
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Chapter and Conference Paper
An Improved Stimulus Reconstruction Method for EEG-Based Short-Time Auditory Attention Detection
Short-time auditory attention detection (AAD) based on electroencephalography (EEG) can be utilized to help hearing-impaired people improve their perception abilities in multi-speaker environments. However, th...
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Chapter and Conference Paper
C2N-ABDP: Cluster-to-Node Attention-Based Differentiable Pooling
Graph neural networks have achieved state-of-the-art performance in various graph based tasks, including classification and regression at both node and graph level. In the context of graph classification, grap...
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Chapter and Conference Paper
MAREPVGG: Multi-attention RepPVGG to Facefake Detection
The threat posed by the increasing means of face forgery and the lowering of the threshold of use is increasing. Although the detection capability of current detection models is improving, most of them need to...
