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Article
Regional filtering distillation for object detection
Knowledge distillation is a common and effective method in model compression, which trains a compact student model to mimic the capability of a large teacher model to get superior generalization. Previous work...
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Chapter and Conference Paper
FGFusion: Fine-Grained Lidar-Camera Fusion for 3D Object Detection
Lidars and cameras are critical sensors that provide complementary information for 3D detection in autonomous driving. While most prevalent methods progressively downscale the 3D point clouds and camera images...
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Article
Uncertain region mining semi-supervised object detection
Semi-supervised learning uses a small amount of labeled data to guide the model and a large amount of unlabeled data to improve its performance. Most semi-supervised object detection methods build a teacher-st...
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Article
Distortion diminishing with vulnerability filters pruning
Overparameterization of convolutional neural networks allows model compression, and model pruning algorithms have attracted much interest due to their practical acceleration effects. The pruning algorithm shou...
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Article
Open AccessSelf-Supervised Learning for Industrial Image Anomaly Detection by Simulating Anomalous Samples
Industrial image anomaly detection (AD) is a critical issue that has been investigated in different research areas. Many works have attempted to detect anomalies by simulating anomalous samples. However, how t...
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Article
GADA-SegNet: gated attentive domain adaptation network for semantic segmentation of LiDAR point clouds
We propose GADA-SegNet, a gated attentive domain adaptation network for semantic segmentation of LiDAR point clouds. Unlike most of existing methods that learn fully from point-wise annotations, our GADA-SegNe...
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Article
Scene text recognition based on two-stage attention and multi-branch feature fusion module
Text image recognition in natural scenes is challenging in computer vision, even though it is already widely used in real-life applications. With the development of deep learning, the accuracy of scene text re...
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Chapter and Conference Paper
A Simplified Student Network with Multi-teacher Feature Fusion for Industrial Defect Detection
Improved industrial defect detection is deemed critical for ensuring high-quality manufacturing processes. Despite the effectiveness of knowledge distillation in detecting defects, there are still challenges i...
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Chapter and Conference Paper
A Task-Aware Dual Similarity Network for Fine-Grained Few-Shot Learning
The goal of fine-grained few-shot learning is to recognize sub-categories under the same super-category by learning few labeled samples. Most of the recent approaches adopt a single similarity measure, that is...
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Chapter and Conference Paper
Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images
Due to the extreme complexity of scale and shape as well as the uncertainty of the predicted location, salient object detection in optical remote sensing images (RSI-SOD) is a very difficult task. The existing...
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Article
Weber’s law based multi-level convolution correlation features for image retrieval
Weber’s law reveals the relationship between human perception and perceptual stimuli. Inspired by the theory, this paper designs a multi-level convolution correlation feature statistic method for image retriev...
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Article
Nodes Deployment Algorithm Based on Data Fusion and Evidence Theory in Wireless Sensor Networks
Wireless sensor networks have been widely researched and developed in recent years. The node deployment problem is a multi-dimensional nonlinear optimization problem with continuous discrete variables. In orde...
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Chapter and Conference Paper
Robust Ensembling Network for Unsupervised Domain Adaptation
Recently, in order to address the unsupervised domain adaptation (UDA) problem, extensive studies have been proposed to achieve transferrable models. Among them, the most prevalent method is adversarial domain...
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Chapter and Conference Paper
Multi-scale Edge-Based U-Shape Network for Salient Object Detection
Deep-learning based salient object detection methods achieve great improvements. However, there are still problems existing in the predictions, such as blurry boundary and inaccurate location, which is mainly ...
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Article
Budget-constraint mechanism for incremental multi-labeling crowdsensing
Machine learning techniques require an enormous amount of high-quality data labeling for more naturally simulating human comprehension. Recently, mobile crowdsensing, as a new paradigm, makes it possible that ...
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Chapter and Conference Paper
A Combination Method of Edge Detection and SVM Filtering for License Plate Extraction
License plate extraction is an important step of License Plate Recognition (LPR) in Intelligent Transportation System. This paper presents a hybrid license plate extraction method which combines edge detection...
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Article
Ensemble selection by GRASP
Ensemble selection, which aims to select a proper subset of the original whole ensemble, can be seen as a combinatorial optimization problem, and usually can achieve a pruned ensemble with better performance t...
