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Attention-based domain adaptation for single-stage detectors
While domain adaptation has been used to improve the performance of object detectors when the training and test data follow different distributions,...
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Relation-Guided Multi-stage Feature Aggregation Network for Video Object Detection
Video object detection task has received extensive research attention and various methods have been proposed. The quality of single frame in the...
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Decoupling and Interaction: task coordination in single-stage object detection
In the field of computer vision, general single-stage object detection methods employ two individual subnets within detection head, serving...
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A paced multi-stage block-wise approach for object detection in thermal images
The growing advocacy of thermal imagery in applications, such as autonomous vehicles, surveillance, and COVID-19 detection, necessitates accurate...
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Multi-task feature-aligned head in one-stage object detection
Existing one-stage detectors usually use two decoupled branches to optimize two subtasks, i.e., object localization and classification. However, this...
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GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation
The inherent ambiguity in ground-truth annotations of 3D bounding boxes, caused by occlusions, signal missing, or manual annotation errors, can...
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CGTracker: Center Graph Network for One-Stage Multi-Pedestrian-Object Detection and Tracking
Most current online multi-object tracking (MOT) methods include two steps: object detection and data association, where the data association step...
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A Comprehensive Study of the Robustness for LiDAR-Based 3D Object Detectors Against Adversarial Attacks
Recent years have witnessed significant advancements in deep learning-based 3D object detection, leading to its widespread adoption in numerous...
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Deep insights on processing strata, features and detectors for fingerprint and iris liveness detection techniques
Fingerprint and iris traits are used in sensitive applications and so, spoofing them can impose a serious security threat as well as financial...
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MEOD: A Robust Multi-stage Ensemble Model Based on Rank Aggregation and Stacking for Outlier Detection
In ensemble-based unsupervised outlier detection, the lack of ground truth makes the combination of basic outlier detectors a challenging task. The...
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RL-MAGE: Strengthening Malware Detectors Against Smart Adversaries
Today, android dominates the smartphone operating systems market. As per Google, there are over 3 billion active android users. With such a large...
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Exploring the efficacy and comparative analysis of one-stage object detectors for computer vision: a review
One-stage object detection is a technique that uses a single deep neural network to detect objects in an image or video. This method trains the...
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A brief review of state-of-the-art object detectors on benchmark document images datasets
Document image analysis (DIA) has become a challenging brand in computer vision, which is the foundation of document understanding applications. Page...
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Underwater autonomous gras** robot based on multi-stage Cascade DetNet
At present, underwater exploration and salvage, underwater archaeology, and other underwater operations still mainly rely on professional underwater...
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A Real-Time Multi-Stage Architecture for Pose Estimation of Zebrafish Head with Convolutional Neural Networks
In order to conduct optical neurophysiology experiments on a freely swimming zebrafish, it is essential to quantify the zebrafish head to determine...
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Towards a Practical Defense Against Adversarial Attacks on Deep Learning-Based Malware Detectors via Randomized Smoothing
Malware detectors based on deep learning (DL) have been shown to be susceptible to malware examples that have been deliberately manipulated in order...
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Rethinking CNN Architectures in Transformer Detectors
Since the introduction of Transformer into the field of object detection, numerous researchers have endeavored to leverage its strong long-distance...
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Distilling Object Detectors with Global Knowledge
Knowledge distillation learns a lightweight student model that mimics a cumbersome teacher. Existing methods regard the knowledge as the feature of...
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Precise Recognition of Vision Based Multi-hand Signs Using Deep Single Stage Convolutional Neural Network
The precise recognition of multi-hand signs in real-time under dynamic backgrounds, illumination conditions is a time consuming process. In this...
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A Lightweight Safety Helmet Detection Network Based on Bidirectional Connection Module and Polarized Self-attention
Safety helmets worn by construction workers in substations can reduce the accident rate in construction operations. With the mature development of...