On the public citation graph

Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors

https://doi.org/10.1109/iccv.2017.379

0 signed · 65 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

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

inferredcites →https://doi.org/10.1007/978-3-319-10590-1_5openalex-snapshot

30Hz Object Detection with DPM V5

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/s11263-009-0275-4openalex-snapshot

The Pascal Visual Object Classes (VOC) Challenge

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

The Pascal Visual Object Classes Challenge: A Retrospective

inferredcites →https://doi.org/10.1016/0306-4573(83)90062-6openalex-snapshot

Introduction to modern information retrieval

inferredcites →https://doi.org/10.1109/cvpr.2001.990517openalex-snapshot

Rapid object detection using a boosted cascade of simple features

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

Histograms of Oriented Gradients for Human Detection

inferredcites →https://doi.org/10.1109/cvpr.2006.119openalex-snapshot

Fast Human Detection Using a Cascade of Histograms of Oriented Gradients

inferredcites →https://doi.org/10.1109/cvpr.2013.237openalex-snapshot

Fast, Accurate Detection of 100,000 Object Classes on a Single Machine

inferredcites →https://doi.org/10.1109/cvpr.2014.320openalex-snapshot

The Fastest Deformable Part Model for Object Detection

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.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.2016.314openalex-snapshot

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2016.376openalex-snapshot

Joint Training of Cascaded CNN for Face Detection

inferredcites →https://doi.org/10.1109/cvpr.2016.650openalex-snapshot

CRAFT Objects from Images

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/iccv.2015.135openalex-snapshot

Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model

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

Fast R-CNN

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

Learning Complexity-Aware Cascades for Deep Pedestrian Detection

inferredcites →https://doi.org/10.1109/icpr.2016.7900006openalex-snapshot

BranchyNet: Fast inference via early exiting from deep neural 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/10.1109/tpami.2016.2577031openalex-snapshot

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

inferredcites →https://doi.org/10.1109/tsmcb.2008.2007853openalex-snapshot

Exploratory Undersampling for Class-Imbalance Learning

inferredcites →https://doi.org/10.1145/2647868.2654889openalex-snapshot

Caffe

inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot

Scikit-learn: Machine Learning in Python

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.1605.06409openalex-snapshot

R-FCN: Object Detection via Region-based Fully Convolutional Networks

inferredcites →https://doi.org/10.5244/c.29.32openalex-snapshot

Real-Time Pedestrian Detection with Deep Network Cascades

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.4832892openalex-extracted

Future Frontiers in Deep Learning: From Static to Dynamic Neural Network Technologies

inferred← citeshttps://doi.org/10.1007/978-3-030-33226-6_15openalex-snapshot

Multimodal Brain Tumor Segmentation Using Encoder-Decoder with Hierarchical Separable Convolution

inferred← citeshttps://doi.org/10.1007/978-3-030-41404-7_29openalex-snapshot

Information Theory-Based Curriculum Learning Factory to Optimize Training

inferred← citeshttps://doi.org/10.1007/s10044-019-00853-9openalex-snapshot

An over-regression suppression method to discriminate occluded objects of same category

inferred← citeshttps://doi.org/10.1007/s11831-026-10498-7openalex-snapshot

Architectural and Performance Analysis of Static and Dynamic Neural Networks in Plant Disease Detection

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2024.109710openalex-snapshot

Neural network developments: A detailed survey from static to dynamic models

inferred← citeshttps://doi.org/10.1016/j.measurement.2025.118052openalex-snapshot

TEGDNet: Texture Enhancement Guided Detection Network for spiral welded pipeline defect detection

inferred← citeshttps://doi.org/10.1109/access.2019.2939201openalex-snapshot

A Survey of Deep Learning-Based Object Detection

inferred← citeshttps://doi.org/10.1109/cvpr.2019.00236openalex-snapshot

ELASTIC: Improving CNNs With Dynamic Scaling Policies

inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.00608openalex-snapshot

DynamicDet: A Unified Dynamic Architecture for Object Detection

inferred← citeshttps://doi.org/10.1109/cvprw.2018.00095openalex-snapshot

It Takes Two to Tango: Cascading off-the-Shelf Face Detectors

inferred← citeshttps://doi.org/10.1109/cvprw59228.2023.00256openalex-snapshot

Dynamic Multimodal Fusion

inferred← citeshttps://doi.org/10.1109/hpec.2019.8916237openalex-snapshot

A Novel Design of Adaptive and Hierarchical Convolutional Neural Networks using Partial Reconfiguration on FPGA

inferred← citeshttps://doi.org/10.1109/icassp39728.2021.9414013openalex-snapshot

Ensembling Object Detectors for Image and Video Data Analysis

inferred← citeshttps://doi.org/10.1109/icme.2018.8486561openalex-snapshot

Cascade Mask Generation Framework for Fast Small Object Detection

inferred← citeshttps://doi.org/10.1109/icoei.2019.8862737openalex-snapshot

Autonomous Robot to Detect Diseased Leaves in Plants using Convolutional Neural Networks

inferred← citeshttps://doi.org/10.1109/icpr.2018.8546088openalex-snapshot

Weakly- and Semi-supervised Faster R-CNN with Curriculum Learning

inferred← citeshttps://doi.org/10.1109/jiot.2025.3594344openalex-snapshot

Optimal Confidence Thresholds for Cooperative Inference in Intelligent Surveillance Systems

inferred← citeshttps://doi.org/10.1109/jiot.2026.3660845openalex-snapshot

Energy-Efficient Cooperative Inference in Multidevice Edge Networks: A Lyapunov-Based Approach

inferred← citeshttps://doi.org/10.1109/jsen.2024.3387281openalex-snapshot

Visual Attention Estimation Algorithm and Dynamic Neural Network Based Object Detection for Intelligent Vehicles

inferred← citeshttps://doi.org/10.1109/les.2024.3514920openalex-snapshot

Optimized Inference Scheme for Conditional Computation in On-Device Object Detection

inferred← citeshttps://doi.org/10.1109/sec62691.2024.00021openalex-snapshot

Optimizing Edge Offloading Decisions for Object Detection

inferred← citeshttps://doi.org/10.1109/spcnc68200.2025.11406565openalex-snapshot

VATES: Video-Adaptive Temporal Enhancement System

inferred← citeshttps://doi.org/10.1109/tgrs.2026.3658431openalex-snapshot

Dynamic Mutual Learning for Object Detection in Aerial Imagery

inferred← citeshttps://doi.org/10.1109/tpami.2021.3117837openalex-snapshot

Dynamic Neural Networks: A Survey

inferred← citeshttps://doi.org/10.1145/3195106.3195120openalex-snapshot

Object Detection with Multi-RCNN Detectors

inferred← citeshttps://doi.org/10.11922/11-6035.csd.2023.0180.zhopenalex-snapshot

An dataset of rural roadway images of instance segmentation in Northern Xinjiang

inferred← citeshttps://doi.org/10.1609/aaai.v35i8.16900openalex-snapshot

Learning to Cascade: Confidence Calibration for Improving the Accuracy and Computational Cost of Cascade Inference Systems

inferred← citeshttps://doi.org/10.3233/atde230123openalex-snapshot

DH-Net: Dynamic Head for Object Detection Network

inferred← citeshttps://doi.org/10.3390/app15042026openalex-snapshot

Detection of Welding Defects Tracked by YOLOv4 Algorithm

inferred← citeshttps://doi.org/10.3390/fi16060213openalex-snapshot

Learning Modality Consistency and Difference Information with Multitask Learning for Multimodal Sentiment Analysis

inferred← citeshttps://doi.org/10.48550/arxiv.1801.00904openalex-snapshot

ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks

inferred← citeshttps://doi.org/10.48550/arxiv.2104.09286openalex-snapshot

Learning to Cascade: Confidence Calibration for Improving the Accuracy and Computational Cost of Cascade Inference Systems

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.