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High-Level Semantic Networks for Multi-Scale Object Detection

https://doi.org/10.1109/tcsvt.2019.2950526

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01219-9_39openalex-snapshot

Occlusion-Aware R-CNN: Detecting Pedestrians in a Crowd

inferredcites →https://doi.org/10.1007/978-3-030-01228-1_20openalex-snapshot

SAN: Learning Relationship Between Convolutional Features for Multi-scale Object Detection

inferredcites →https://doi.org/10.1007/978-3-030-01249-6_17openalex-snapshot

ExFuse: Enhancing Feature Fusion for Semantic Segmentation

inferredcites →https://doi.org/10.1007/978-3-030-01264-9_48openalex-snapshot

Acquisition of Localization Confidence for Accurate Object Detection

inferredcites →https://doi.org/10.1007/978-3-319-10602-1_48openalex-snapshot

Microsoft COCO: Common Objects in Context

inferredcites →https://doi.org/10.1007/978-3-319-46475-6_28openalex-snapshot

Is Faster R-CNN Doing Well for Pedestrian Detection?

inferredcites →https://doi.org/10.1007/978-3-319-46493-0_22openalex-snapshot

A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

inferredcites →https://doi.org/10.1007/978-3-319-54184-6_26openalex-snapshot

Scale-Adaptive Deconvolutional Regression Network for Pedestrian Detection

inferredcites →https://doi.org/10.1007/s11263-014-0733-5openalex-snapshot

The Pascal Visual Object Classes Challenge: A Retrospective

inferredcites →https://doi.org/10.1007/s11432-018-9497-0openalex-snapshot

GlanceNets — efficient convolutional neural networks with adaptive hard example mining

inferredcites →https://doi.org/10.1023/b:visi.0000013087.49260.fbopenalex-snapshot

Robust Real-Time Face Detection

inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot

Deep learning

inferredcites →https://doi.org/10.1109/cvpr.2005.177openalex-snapshot

Histograms of Oriented Gradients for Human Detection

inferredcites →https://doi.org/10.1109/cvpr.2012.6248017openalex-snapshot

Pedestrian detection at 100 frames per second

inferredcites →https://doi.org/10.1109/cvpr.2012.6248074openalex-snapshot

Are we ready for autonomous driving? The KITTI vision benchmark suite

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.2015.7298784openalex-snapshot

Filtered channel features for pedestrian detection

inferredcites →https://doi.org/10.1109/cvpr.2015.7299034openalex-snapshot

Taking a deeper look at pedestrians

inferredcites →https://doi.org/10.1109/cvpr.2015.7299143openalex-snapshot

Pedestrian detection aided by deep learning semantic tasks

inferredcites →https://doi.org/10.1109/cvpr.2015.7299170openalex-snapshot

A convolutional neural network cascade for face detection

inferredcites →https://doi.org/10.1109/cvpr.2016.141openalex-snapshot

How Far are We from Solving Pedestrian Detection?

inferredcites →https://doi.org/10.1109/cvpr.2016.234openalex-snapshot

Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers

inferredcites →https://doi.org/10.1109/cvpr.2016.236openalex-snapshot

Monocular 3D Object Detection for Autonomous Driving

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.2016.350openalex-snapshot

The Cityscapes Dataset for Semantic Urban Scene Understanding

inferredcites →https://doi.org/10.1109/cvpr.2016.596openalex-snapshot

WIDER FACE: A Face Detection Benchmark

inferredcites →https://doi.org/10.1109/cvpr.2016.89openalex-snapshot

Training Region-Based Object Detectors with Online Hard Example Mining

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

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/cvpr.2017.106openalex-snapshot

Feature Pyramid Networks for Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2017.166openalex-snapshot

Finding Tiny Faces

inferredcites →https://doi.org/10.1109/cvpr.2017.207openalex-snapshot

Scale-Aware Face Detection

inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot

Densely Connected Convolutional Networks

inferredcites →https://doi.org/10.1109/cvpr.2017.324openalex-snapshot

A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2017.351openalex-snapshot

Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors

inferredcites →https://doi.org/10.1109/cvpr.2017.474openalex-snapshot

CityPersons: A Diverse Dataset for Pedestrian Detection

inferredcites →https://doi.org/10.1109/cvpr.2017.557openalex-snapshot

RON: Reverse Connection with Objectness Prior Networks for Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2017.634openalex-snapshot

Aggregated Residual Transformations for Deep Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2017.639openalex-snapshot

What Can Help Pedestrian Detection?

inferredcites →https://doi.org/10.1109/cvpr.2017.660openalex-snapshot

Pyramid Scene Parsing Network

inferredcites →https://doi.org/10.1109/cvpr.2017.75openalex-snapshot

Dilated Residual Networks

inferredcites →https://doi.org/10.1109/cvpr.2017.87openalex-snapshot

Accurate Single Stage Detector Using Recurrent Rolling Convolution

inferredcites →https://doi.org/10.1109/cvpr.2018.00136openalex-snapshot

Multi-scale Location-Aware Kernel Representation for Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2018.00377openalex-snapshot

An Analysis of Scale Invariance in Object Detection - SNIP

inferredcites →https://doi.org/10.1109/cvpr.2018.00644openalex-snapshot

Cascade R-CNN: Delving Into High Quality Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2018.00745openalex-snapshot

Squeeze-and-Excitation Networks

inferredcites →https://doi.org/10.1109/cvpr.2018.00811openalex-snapshot

Repulsion Loss: Detecting Pedestrians in a Crowd

inferredcites →https://doi.org/10.1109/cvpr.2019.00740openalex-snapshot

Pedestrian Detection With Autoregressive Network Phases

inferredcites →https://doi.org/10.1109/cvpr.2019.00757openalex-snapshot

Triply Supervised Decoder Networks for Joint Detection and Segmentation

inferredcites →https://doi.org/10.1109/cvprw.2017.259openalex-snapshot

Multi-scale Fully Convolutional Network for Face Detection in the Wild

inferredcites →https://doi.org/10.1109/iccv.2015.169openalex-snapshot

Fast R-CNN

inferredcites →https://doi.org/10.1109/iccv.2015.18openalex-snapshot

Convolutional Channel Features

inferredcites →https://doi.org/10.1109/iccv.2015.221openalex-snapshot

Deep Learning Strong Parts for Pedestrian Detection

inferredcites →https://doi.org/10.1109/iccv.2015.384openalex-snapshot

Learning Complexity-Aware Cascades for Deep Pedestrian Detection

inferredcites →https://doi.org/10.1109/iccv.2017.30openalex-snapshot

S^3FD: Single Shot Scale-Invariant Face Detector

inferredcites →https://doi.org/10.1109/iccv.2017.322openalex-snapshot

Mask R-CNN

inferredcites →https://doi.org/10.1109/iccv.2017.324openalex-snapshot

Focal Loss for Dense Object Detection

inferredcites →https://doi.org/10.1109/iccv.2017.444openalex-snapshot

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

inferredcites →https://doi.org/10.1109/iccv.2017.522openalex-snapshot

SSH: Single Stage Headless Face Detector

inferredcites →https://doi.org/10.1109/iccv.2019.00615openalex-snapshot

Scale-Aware Trident Networks for Object Detection

inferredcites →https://doi.org/10.1109/tcsvt.2016.2630731openalex-snapshot

Incremental Learning With Saliency Map for Moving Object Detection

inferredcites →https://doi.org/10.1109/tcsvt.2017.2648850openalex-snapshot

Pushing the Limits of Deep CNNs for Pedestrian Detection

inferredcites →https://doi.org/10.1109/tcsvt.2017.2730258openalex-snapshot

Improved Object Detection With Iterative Localization Refinement in Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/tcsvt.2017.2778227openalex-snapshot

Toward a Comprehensive Face Detector in the Wild

inferredcites →https://doi.org/10.1109/tcyb.2016.2601438openalex-snapshot

Cascade Learning by Optimally Partitioning

inferredcites →https://doi.org/10.1109/tip.2016.2609807openalex-snapshot

Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry

inferredcites →https://doi.org/10.1109/tip.2017.2694224openalex-snapshot

Learning Multilayer Channel Features for Pedestrian Detection

inferredcites →https://doi.org/10.1109/tmm.2017.2759508openalex-snapshot

Scale-aware Fast R-CNN for Pedestrian Detection

inferredcites →https://doi.org/10.1109/tnnls.2017.2676130openalex-snapshot

Convolution in Convolution for Network in Network

inferredcites →https://doi.org/10.1109/tpami.2011.155openalex-snapshot

Pedestrian Detection: An Evaluation of the State of the Art

inferredcites →https://doi.org/10.1109/tpami.2014.2300479openalex-snapshot

Fast Feature Pyramids for Object Detection

inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

inferredcites →https://doi.org/10.1109/tpami.2017.2699184openalex-snapshot

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

inferredcites →https://doi.org/10.1109/tpami.2019.2913372openalex-snapshot

Squeeze-and-Excitation Networks

inferredcites →https://doi.org/10.1109/wacv.2017.108openalex-snapshot

Subcategory-Aware Convolutional Neural Networks for Object Proposals and Detection

inferredcites →https://doi.org/10.1109/wacv.2017.111openalex-snapshot

Fused DNN: A Deep Neural Network Fusion Approach to Fast and Robust Pedestrian Detection

inferredcites →https://doi.org/10.1145/3065386openalex-snapshot

ImageNet classification with deep convolutional neural networks

inferredcites →https://doi.org/10.1609/aaai.v31i1.11231openalex-snapshot

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

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.1612.06851openalex-snapshot

Beyond Skip Connections: Top-Down Modulation for Object Detection

inferredcites →https://doi.org/10.48550/arxiv.1701.06659openalex-snapshot

DSSD : Deconvolutional Single Shot Detector

inferredcites →https://doi.org/10.48550/arxiv.1706.05587openalex-snapshot

Rethinking Atrous Convolution for Semantic Image Segmentation

inferredcites →https://doi.org/10.5244/c.23.91openalex-snapshot

Integral Channel Features

inferredcites →https://doi.org/10.5244/c.30.15openalex-snapshot

A MultiPath Network for Object Detection

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Count- and Similarity-Aware R-CNN for Pedestrian Detection

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GP-HLS: Gaussian Process-Based Unsupervised High-Level Semantics Representation Learning of Multivariate Time Series

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A context- and level-aware feature pyramid network for object detection with attention mechanism

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Recurrent self-optimizing proposals for weakly supervised object detection

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Feature enhancement modules applied to a feature pyramid network for object detection

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Attention-based fusion factor in FPN for object detection

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Weakly-supervised object localization with gradient-pyramid feature

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SE-YOLOv4: shuffle expansion YOLOv4 for pedestrian detection based on PixelShuffle

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Stacked Pyramid Attention Network for Object Detection

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PSC-Net: learning part spatial co-occurrence for occluded pedestrian detection

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Edge-aware Feature Aggregation Network for Polyp Segmentation

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MSEIGDNet: a muti-scale edge information generation and diffusion network for UAV aerial images

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Pre-locate net for object detection in high-resolution images

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Hierarchical Meta Alignment for cross-domain object detection

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Info-FPN: An Informative Feature Pyramid Network for object detection in remote sensing images

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Feature aggregation network for small object detection

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Reasoning, Perception, and Control with Vision Language Models: A Survey of Multimodal Learning for Autonomous Vehicles

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Reliable hybrid knowledge distillation for multi-source domain adaptive object detection

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Hybrid channel based pedestrian detection

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Low-Power Portable System for Power Grid Foreign Object Detection Based on the Lightweight Model of Improved YOLOv7

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Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians

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Robust Small-scale Pedestrian Detection with Cued Recall via Memory Learning

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A Multi-correspondence Object Detection Algorithm Based on Keypoints

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GSINet: Gradual Semantic Interaction Network for Remote Sensing Object Detection Based on Dual Attention Mechanism

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Image Impulse Noise Removal Using Cascaded Filtering Based on Overlapped Adaptive Gaussian Smoothing and Convolutional Refinement Networks

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Efficient Selective Context Network for Accurate Object Detection

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CUA Loss: Class Uncertainty-Aware Gradient Modulation for Robust Object Detection

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2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30

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Uncertainty-Guided Cross-Modal Learning for Robust Multispectral Pedestrian Detection

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Stereo Refinement Dehazing Network

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BiCSNet: A Bidirectional Cross-Scale Backbone for Recognition and Localization

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Multi-Source Aggregation Transformer for Concealed Object Detection in Millimeter-Wave Images

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Improving Multiscale Object Detection With Off-Centered Semantics Refinement

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Spatial-Temporal Based Multihead Self-Attention for Remote Sensing Image Change Detection

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Dense Crosstalk Feature Aggregation for Classification and Localization in Object Detection

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A Dense-Aware Cross-splitNet for Object Detection and Recognition

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Bridging Multi-Scale Context-Aware Representation for Object Detection

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Tiny Object Detection via Regional Cross Self-Attention Network

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Balanced One-Stage Object Detection by Enhancing the Effect of Positive Samples

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Save the Tiny, Save the All: Hierarchical Activation Network for Tiny Object Detection

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Bi-Directional and Triangular Circulation Fusion Neural Networks for Small Object Detection

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Automatic Label Assignment for Object Detection

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Feature Split–Merge–Enhancement Network for Remote Sensing Object Detection

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GCWNet: A Global Context-Weaving Network for Object Detection in Remote Sensing Images

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MLFFNet: Multilevel Feature Fusion Network for Object Detection in Sonar Images

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Global to Local: A Scale-Aware Network for Remote Sensing Object Detection

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Attention-Free Global Multiscale Fusion Network for Remote Sensing Object Detection

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Boundary-Aware Feature Fusion With Dual-Stream Attention for Remote Sensing Small Object Detection

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PurifyNet: A Robust Person Re-Identification Model With Noisy Labels

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TJU-DHD: A Diverse High-Resolution Dataset for Object Detection

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Region-Object Relation-Aware Dense Captioning via Transformer

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Effective Fusion Factor in FPN for Tiny Object Detection

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Multi-scale feature fusion algorithm for underwater remote object detection in forward-looking sonar images

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Semantic Relationship-Based Unsupervised Representation Learning of Multivariate Time Series

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MI2T-UNet: Integrating Multi-Scale Input Image and Top-hat Context for Infrared Small Object Segmentation

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MI²T-UNet: Integrating Multi-Scale Input Image and Top-hat Context for Infrared Small Object Segmentation

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Attention-based Weighted Fusion Network for Object Detection

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Marine Ship Detection Based on Twin Feature Pyramid Network and Spatial Attention

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Multi-Task Learning for UAV Aerial Object Detection in Foggy Weather Condition

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A Refined and Efficient CNN Algorithm for Remote Sensing Object Detection

inferred← citeshttps://doi.org/10.3934/mbe.2023282openalex-snapshot

Deep learning-based small object detection: A survey

inferred← citeshttps://doi.org/10.3934/mbe.2023746openalex-snapshot

AGMG-Net: Leveraging multiscale and fine-grained features for improved cargo recognition

inferred← citeshttps://doi.org/10.5755/j01.itc.53.2.35569openalex-snapshot

YOLOv7-PD: Incorporating DE-ELAN and NWD-CIoU for Advanced Pedestrian Detection Method

inferred← citeshttps://doi.org/10.7717/peerj-cs.2570openalex-snapshot

Foreign object debris detection in lane images using deep learning methodology

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