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CRAFT Objects from Images
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-10578-9_23openalex-snapshot
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
inferredcites →https://doi.org/10.1007/978-3-319-10602-1_26openalex-snapshot
Edge Boxes: Locating Object Proposals from Edges
inferredcites →https://doi.org/10.1007/978-3-642-15561-1_11openalex-snapshot
Improving the Fisher Kernel for Large-Scale Image Classification
inferredcites →https://doi.org/10.1007/s10994-009-5108-8openalex-snapshot
Cutting-plane training of structural SVMs
inferredcites →https://doi.org/10.1007/s11263-013-0620-5openalex-snapshot
Selective Search for Object Recognition
inferredcites →https://doi.org/10.1023/b:visi.0000013087.49260.fbopenalex-snapshot
Robust Real-Time Face Detection
inferredcites →https://doi.org/10.1023/b:visi.0000022288.19776.77openalex-snapshot
Efficient Graph-Based Image Segmentation
inferredcites →https://doi.org/10.1109/34.655647openalex-snapshot
Neural network-based face detection
inferredcites →https://doi.org/10.1109/cvpr.1996.517075openalex-snapshot
Neural network-based face detection
inferredcites →https://doi.org/10.1109/cvpr.1997.609319openalex-snapshot
Pedestrian detection using wavelet templates
inferredcites →https://doi.org/10.1109/cvpr.2005.177openalex-snapshot
Histograms of Oriented Gradients for Human Detection
inferredcites →https://doi.org/10.1109/cvpr.2014.276openalex-snapshot
Scalable Object Detection Using Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2014.414openalex-snapshot
BING: Binarized Normed Gradients for Objectness Estimation at 300fps
inferredcites →https://doi.org/10.1109/cvpr.2014.49openalex-snapshot
Multiscale Combinatorial Grouping
inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2015.7298854openalex-snapshot
DeepID-Net: Deformable deep convolutional neural networks for object detection
inferredcites →https://doi.org/10.1109/cvpr.2015.7299146openalex-snapshot
Object detection by labeling superpixels
inferredcites →https://doi.org/10.1109/cvpr.2016.314openalex-snapshot
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2016.343openalex-snapshot
Instance-Aware Semantic Segmentation via Multi-task Network Cascades
inferredcites →https://doi.org/10.1109/cvpr.2016.91openalex-snapshot
You Only Look Once: Unified, Real-Time Object Detection
inferredcites →https://doi.org/10.1109/iccv.2015.135openalex-snapshot
Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model
inferredcites →https://doi.org/10.1109/iccv.2015.285openalex-snapshot
DeepBox: Learning Objectness with Convolutional Networks
inferredcites →https://doi.org/10.1109/tpami.2009.167openalex-snapshot
Object Detection with Discriminatively Trained Part-Based Models
inferredcites →https://doi.org/10.1109/tpami.2015.2389824openalex-snapshot
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
inferredcites →https://doi.org/10.1145/1553374.1553523openalex-snapshot
Learning structural SVMs with latent variables
inferredcites →https://doi.org/10.1214/aos/1016218223openalex-snapshot
Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)
inferredcites →https://doi.org/10.48550/arxiv.1312.2249openalex-snapshot
Scalable Object Detection using Deep Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1406.6962openalex-snapshot
How good are detection proposals, really?
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1409.3505openalex-snapshot
DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection
inferredcites →https://doi.org/10.48550/arxiv.1409.4842openalex-snapshot
Going Deeper with Convolutions
inferredcites →https://doi.org/10.48550/arxiv.1506.04878openalex-snapshot
End-to-end people detection in crowded scenes
inferredcites →https://doi.org/10.5244/c.25.76openalex-snapshot
The devil is in the details: an evaluation of recent feature encoding methods
inferredcites →https://doi.org/10.57702/o9raffedopenalex-snapshot
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-030-01237-3_30openalex-snapshot
Video Object Detection with an Aligned Spatial-Temporal Memory
inferred← citeshttps://doi.org/10.1007/978-3-030-01264-9_48openalex-snapshot
Acquisition of Localization Confidence for Accurate Object Detection
inferred← citeshttps://doi.org/10.1007/978-3-030-58523-5_31openalex-snapshot
Learning to Separate: Detecting Heavily-Occluded Objects in Urban Scenes
inferred← citeshttps://doi.org/10.1007/978-3-030-58589-1_26openalex-snapshot
Mining Inter-Video Proposal Relations for Video Object Detection
inferred← citeshttps://doi.org/10.1007/978-3-030-70665-4_105openalex-snapshot
Research on Target Detection Method Based on Deep Learning
inferred← citeshttps://doi.org/10.1007/978-3-319-69923-3_65openalex-snapshot
Rich Features and Precise Localization with Region Proposal Network for Object Detection
inferred← citeshttps://doi.org/10.1007/978-3-319-70090-8_2openalex-snapshot
Revisiting Faster R-CNN: A Deeper Look at Region Proposal Network
inferred← citeshttps://doi.org/10.1007/978-981-10-5152-4_2openalex-snapshot
Deep Learning in Object Detection
inferred← citeshttps://doi.org/10.1007/978-981-97-4928-7_6openalex-snapshot
Latent Dirichlet Allocation for Topic Modeling and Intelligent Document Classification
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Finding hard faces with better proposals and classifier
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Making accurate object detection at the edge: review and new approach
inferred← citeshttps://doi.org/10.1007/s10462-025-11284-wopenalex-snapshot
Comprehensive review of recent developments in visual object detection based on deep learning
inferred← citeshttps://doi.org/10.1007/s11042-018-5875-yopenalex-snapshot
Cascaded one-vs-rest detection network for fine-grained recognition without part annotations
inferred← citeshttps://doi.org/10.1007/s11042-018-6240-xopenalex-snapshot
Multi-scale pedestrian detection using skip pooling and recurrent convolution
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Art painting detection and identification based on deep learning and image local features
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Recent progresses on object detection: a brief review
inferred← citeshttps://doi.org/10.1007/s11042-020-09503-3openalex-snapshot
C-FCN: Corners-based fully convolutional network for visual object detection
inferred← citeshttps://doi.org/10.1007/s11042-022-13424-8openalex-snapshot
Improving apparel detection with category grouping and multi-grained branches
inferred← citeshttps://doi.org/10.1007/s11263-019-01247-4openalex-snapshot
Deep Learning for Generic Object Detection: A Survey
inferred← citeshttps://doi.org/10.1016/b978-0-12-810408-8.00003-1openalex-snapshot
An Introduction to Deep Convolutional Neural Nets for Computer Vision
inferred← citeshttps://doi.org/10.1016/j.cosrev.2020.100301openalex-snapshot
A comprehensive and systematic look up into deep learning based object detection techniques: A review
inferred← citeshttps://doi.org/10.1016/j.engappai.2020.103615openalex-snapshot
STDnet: Exploiting high resolution feature maps for small object detection
inferred← citeshttps://doi.org/10.1016/j.engappai.2026.115347openalex-snapshot
Diffusion-based domain-anchored refinement for vehicle dent detection enhancement
inferred← citeshttps://doi.org/10.1016/j.eswa.2021.116105openalex-snapshot
A comprehensive and systematic review on classical and deep learning based region proposal algorithms
inferred← citeshttps://doi.org/10.1016/j.inpa.2020.01.002openalex-snapshot
Segmentation and measurement scheme for fish morphological features based on Mask R-CNN
inferred← citeshttps://doi.org/10.1016/j.jvcir.2018.11.014openalex-snapshot
Detection and tracking based tubelet generation for video object detection
inferred← citeshttps://doi.org/10.1016/j.jvcir.2019.02.020openalex-snapshot
You always look again: Learning to detect the unseen objects
inferred← citeshttps://doi.org/10.1016/j.knosys.2021.107083openalex-snapshot
An anchor-free object detector with novel corner matching method
inferred← citeshttps://doi.org/10.1016/j.knosys.2022.108136openalex-snapshot
Multi-level knowledge distillation for low-resolution object detection and facial expression recognition
inferred← citeshttps://doi.org/10.1016/j.neucom.2017.06.008openalex-snapshot
ListNet-based object proposals ranking
inferred← citeshttps://doi.org/10.1016/j.neucom.2017.12.070openalex-snapshot
Cascade region proposal and global context for deep object detection
inferred← citeshttps://doi.org/10.1016/j.neucom.2019.08.016openalex-snapshot
C-RPNs: Promoting object detection in real world via a cascade structure of Region Proposal Networks
inferred← citeshttps://doi.org/10.1016/j.neucom.2020.01.085openalex-snapshot
Recent advances in deep learning for object detection
inferred← citeshttps://doi.org/10.1016/j.neucom.2020.05.027openalex-snapshot
Video object detection for autonomous driving: Motion-aid feature calibration
inferred← citeshttps://doi.org/10.1016/j.neucom.2020.10.098openalex-snapshot
Delving deep into the imbalance of positive proposals in two-stage object detection
inferred← citeshttps://doi.org/10.1016/j.neucom.2021.04.083openalex-snapshot
Group multi-scale attention pyramid network for traffic sign detection
inferred← citeshttps://doi.org/10.1016/j.patcog.2022.108781openalex-snapshot
Residual objectness for imbalance reduction
inferred← citeshttps://doi.org/10.1016/j.patrec.2017.09.040openalex-snapshot
D-STC: Deep learning with spatio-temporal constraints for train drivers detection from videos
inferred← citeshttps://doi.org/10.1109/access.2018.2825229openalex-snapshot
Road Object Detection Using a Disparity-Based Fusion Model
inferred← citeshttps://doi.org/10.1109/access.2019.2929432openalex-snapshot
Part-Aware Region Proposal for Vehicle Detection in High Occlusion Environment
inferred← citeshttps://doi.org/10.1109/access.2020.3002860openalex-snapshot
Improved Target Detection Algorithm Based on Libra R-CNN
inferred← citeshttps://doi.org/10.1109/access.2022.3166923openalex-snapshot
ALODAD: An Anchor-Free Lightweight Object Detector for Autonomous Driving
inferred← citeshttps://doi.org/10.1109/cac.2018.8623808openalex-snapshot
Object Detection Using Deep Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1109/cvpr.2017.351openalex-snapshot
Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors
inferred← citeshttps://doi.org/10.1109/cvpr.2017.441openalex-snapshot
Deep Feature Flow for Video Recognition
inferred← citeshttps://doi.org/10.1109/cvpr.2017.539openalex-snapshot
All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation
inferred← citeshttps://doi.org/10.1109/cvpr.2018.00427openalex-snapshot
Pseudo Mask Augmented Object Detection
inferred← citeshttps://doi.org/10.1109/cvpr.2018.00644openalex-snapshot
Cascade R-CNN: Delving Into High Quality Object Detection
inferred← citeshttps://doi.org/10.1109/cvpr.2019.00308openalex-snapshot
Region Proposal by Guided Anchoring
inferred← citeshttps://doi.org/10.1109/cvpr.2019.00511openalex-snapshot
Hybrid Task Cascade for Instance Segmentation
inferred← citeshttps://doi.org/10.1109/cvpr.2019.00968openalex-snapshot
Object Detection With Location-Aware Deformable Convolution and Backward Attention Filtering
inferred← citeshttps://doi.org/10.1109/cvpr42600.2020.01320openalex-snapshot
Offset Bin Classification Network for Accurate Object Detection
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.00818openalex-snapshot
Deeply Shape-guided Cascade for Instance Segmentation
inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.00519openalex-snapshot
Recurrent Glimpse-based Decoder for Detection with Transformer
inferred← citeshttps://doi.org/10.1109/cvprw.2017.56openalex-snapshot
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
inferred← citeshttps://doi.org/10.1109/icassp40776.2020.9054080openalex-snapshot
Spatial-Temporal Feature Aggregation Network For Video Object Detection
inferred← citeshttps://doi.org/10.1109/iccv.2017.214openalex-snapshot
Chained Cascade Network for Object Detection
inferred← citeshttps://doi.org/10.1109/iccv.2017.379openalex-snapshot
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
inferred← citeshttps://doi.org/10.1109/iccv.2019.00712openalex-snapshot
Relation Distillation Networks for Video Object Detection
inferred← citeshttps://doi.org/10.1109/icfhr.2016.0075openalex-snapshot
Region Proposal for Pattern Spotting in Historical Document Images
inferred← citeshttps://doi.org/10.1109/icme.2019.00300openalex-snapshot
Cascade Region Proposal Networks for Object Detection in the Wild
inferred← citeshttps://doi.org/10.1109/icwcsg53609.2021.00045openalex-snapshot
YOLOv5_CSL_F: YOLOv5’s Loss Improvement and Attention Mechanism Application for Remote Sensing Image Object Detection
inferred← citeshttps://doi.org/10.1109/iros.2017.8202234openalex-snapshot
Car detection for autonomous vehicle: LIDAR and vision fusion approach through deep learning framework
inferred← citeshttps://doi.org/10.1109/ithings/greencom/cpscom/smartdata.2019.00031openalex-snapshot
Multi-Scale Feature Pair Based R-CNN Method for Defect Detection
inferred← citeshttps://doi.org/10.1109/jstars.2021.3085665openalex-snapshot
Learning Higher Quality Rotation Invariance Features for Multioriented Object Detection in Remote Sensing Images
inferred← citeshttps://doi.org/10.1109/lra.2022.3146922openalex-snapshot
Learning Open-World Object Proposals Without Learning to Classify
inferred← citeshttps://doi.org/10.1109/mvip49855.2020.9116912openalex-snapshot
Region Proposal Generation: A Hierarchical Merging Similarity-Based Algorithm
inferred← citeshttps://doi.org/10.1109/r10-htc57504.2023.10461784openalex-snapshot
Unlocking the Potential of NLP-Based Machine Learning for Efficient Document Classification
inferred← citeshttps://doi.org/10.1109/tai.2021.3107807openalex-snapshot
Playing Against Deep-Neural-Network-Based Object Detectors: A Novel Bidirectional Adversarial Attack Approach
inferred← citeshttps://doi.org/10.1109/tcsvt.2017.2736553openalex-snapshot
T-CNN: Tubelets With Convolutional Neural Networks for Object Detection From Videos
inferred← citeshttps://doi.org/10.1109/tcsvt.2020.3042219openalex-snapshot
CUA Loss: Class Uncertainty-Aware Gradient Modulation for Robust Object Detection
inferred← citeshttps://doi.org/10.1109/tcyb.2019.2894261openalex-snapshot
Temporally Identity-Aware SSD With Attentional LSTM
inferred← citeshttps://doi.org/10.1109/tim.2023.3273655openalex-snapshot
Online Progressive Instance-Balanced Sampling for Weakly Supervised Vibration Damper Detection
inferred← citeshttps://doi.org/10.1109/tip.2018.2881920openalex-snapshot
CNN Fixations: An Unraveling Approach to Visualize the Discriminative Image Regions
inferred← citeshttps://doi.org/10.1109/tip.2022.3157453openalex-snapshot
BooDet: Gradient Boosting Object Detection With Additive Learning-Based Prediction Aggregation
inferred← citeshttps://doi.org/10.1109/tip.2022.3201469openalex-snapshot
3D Cascade RCNN: High Quality Object Detection in Point Clouds
inferred← citeshttps://doi.org/10.1109/tits.2018.2886283openalex-snapshot
Traffic Sign Detection Using a Multi-Scale Recurrent Attention Network
inferred← citeshttps://doi.org/10.1109/tits.2019.2956813openalex-snapshot
Overcoming Occlusion in the Automotive Environment—A Review
inferred← citeshttps://doi.org/10.1109/tits.2020.3033569openalex-snapshot
Nighttime Road Scene Parsing by Unsupervised Domain Adaptation
inferred← citeshttps://doi.org/10.1109/tmm.2019.2912703openalex-snapshot
Distributed and Efficient Object Detection via Interactions Among Devices, Edge, and Cloud
inferred← citeshttps://doi.org/10.1109/tnnls.2018.2856096openalex-snapshot
Semantically Modeling of Object and Context for Categorization
inferred← citeshttps://doi.org/10.1109/tnnls.2018.2876865openalex-snapshot
Object Detection With Deep Learning: A Review
inferred← citeshttps://doi.org/10.1109/tpami.2017.2745563openalex-snapshot
Crafting GBD-Net for Object Detection
inferred← citeshttps://doi.org/10.1109/tpami.2019.2956516openalex-snapshot
Cascade R-CNN: High Quality Object Detection and Instance Segmentation
inferred← citeshttps://doi.org/10.1109/tpami.2023.3342120openalex-snapshot
CenterNet++ for Object Detection
inferred← citeshttps://doi.org/10.1109/tvt.2021.3049805openalex-snapshot
FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection
inferred← citeshttps://doi.org/10.1111/exsy.70020openalex-snapshot
Cutting‐Edge Deep Learning Methods for Image‐Based Object Detection in Autonomous Driving: In‐Depth Survey
inferred← citeshttps://doi.org/10.1117/12.2305330openalex-snapshot
Border-oriented post-processing refinement on detected vehicle bounding box for ADAS
inferred← citeshttps://doi.org/10.1145/3418205openalex-snapshot
ISDNet: AI-enabled Instance Segmentation of Aerial Scenes for Smart Cities
inferred← citeshttps://doi.org/10.1145/3604078.3604126openalex-snapshot
Deep Learning object detection models: evolution and evaluation
inferred← citeshttps://doi.org/10.1145/3622896.3622911openalex-snapshot
Learning High-Quality Bounding Box for Rotated Object Detection via Rotated Cascade Region Proposal Network
inferred← citeshttps://doi.org/10.1371/journal.pone.0267863openalex-snapshot
Improving object detection quality with structural constraints
inferred← citeshttps://doi.org/10.18280/ts.380228openalex-snapshot
A Region-Based Efficient Network for Accurate Object Detection
inferred← citeshttps://doi.org/10.31812/123456789/7402openalex-snapshot
Proceedings of the 3rd Edge Computing Workshop. Zhytomyr, Ukraine, April 7, 2023
inferred← citeshttps://doi.org/10.3233/jifs-179155openalex-snapshot
RETRACTED: Real-time detection of cole diseases and insect pests in wireless sensor networks
inferred← citeshttps://doi.org/10.3389/fpls.2018.01162openalex-snapshot
High-Performance Deep Neural Network-Based Tomato Plant Diseases and Pests Diagnosis System With Refinement Filter Bank
inferred← citeshttps://doi.org/10.3390/app12125972openalex-snapshot
AspectNet: Aspect-Aware Anchor-Free Detector for Autonomous Driving
inferred← citeshttps://doi.org/10.3390/fi11010009openalex-snapshot
Object Detection Network Based on Feature Fusion and Attention Mechanism
inferred← citeshttps://doi.org/10.3390/rs12193140openalex-snapshot
Object Detection in UAV Images via Global Density Fused Convolutional Network
inferred← citeshttps://doi.org/10.3390/s18072335openalex-snapshot
Rapid Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion in Fully Convolutional Neural Networks
inferred← citeshttps://doi.org/10.3390/sym10090375openalex-snapshot
Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks
inferred← citeshttps://doi.org/10.48550/arxiv.1611.06467openalex-snapshot
On The Stability of Video Detection and Tracking
inferred← citeshttps://doi.org/10.48550/arxiv.1702.05711openalex-snapshot
Zoom Out-and-In Network with Recursive Training for Object Proposal
inferred← citeshttps://doi.org/10.48550/arxiv.1702.07054openalex-snapshot
Learning Chained Deep Features and Classifiers for Cascade in Object Detection
inferred← citeshttps://doi.org/10.48550/arxiv.1702.08692openalex-snapshot
Cascade one-vs-rest detection network for fine-grained recognition without part annotations
inferred← citeshttps://doi.org/10.48550/arxiv.1803.05858openalex-snapshot
Pseudo Mask Augmented Object Detection
inferred← citeshttps://doi.org/10.48550/arxiv.1810.04002openalex-snapshot
Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection
inferred← citeshttps://doi.org/10.48550/arxiv.1907.11914openalex-snapshot
Rethinking Classification and Localization for Cascade R-CNN
inferred← citeshttps://doi.org/10.48550/arxiv.1909.13522openalex-snapshot
EdgeCNN: Convolutional Neural Network Classification Model with small inputs for Edge Computing
inferred← citeshttps://doi.org/10.48550/arxiv.2005.11472openalex-snapshot
Delving into the Imbalance of Positive Proposals in Two-stage Object Detection
inferred← citeshttps://doi.org/10.5244/c.30.44openalex-snapshot
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
inferred← citeshttps://doi.org/10.55056/ceur-ws.org/vol-3374/paper03.pdfopenalex-snapshot
Object detection method based on aerial image instance segmentation received by unmanned aerial vehicles in the conditions rough for visualization
inferred← citeshttps://doi.org/10.55056/jec.749openalex-snapshot
Object detection method based on instance segmentation of satellite image obtained in the conditions of cloud cover
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