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Object-Proposal Evaluation Protocol is ‘Gameable’

https://doi.org/10.1109/cvpr.2016.97

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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_48openalex-snapshot

Spatio-temporal Object Detection Proposals

inferredcites →https://doi.org/10.1007/978-3-319-10584-0_23openalex-snapshot

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

inferredcites →https://doi.org/10.1007/978-3-319-10602-1_26openalex-snapshot

Edge Boxes: Locating Object Proposals from Edges

inferredcites →https://doi.org/10.1007/978-3-319-10602-1_47openalex-snapshot

Geodesic Object Proposals

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

Microsoft COCO: Common Objects in Context

inferredcites →https://doi.org/10.1007/978-3-540-88682-2_4openalex-snapshot

Learning Spatial Context: Using Stuff to Find Things

inferredcites →https://doi.org/10.1007/978-3-642-33715-4_54openalex-snapshot

Indoor Segmentation and Support Inference from RGBD Images

inferredcites →https://doi.org/10.1007/978-3-642-33783-3_4openalex-snapshot

Contextual Object Detection Using Set-Based Classification

inferredcites →https://doi.org/10.1007/978-3-642-33786-4_32openalex-snapshot

Semantic Segmentation with Second-Order Pooling

inferredcites →https://doi.org/10.1007/978-3-642-33885-4_27openalex-snapshot

On Recognizing Actions in Still Images via Multiple Features

inferredcites →https://doi.org/10.1007/s11263-012-0538-3openalex-snapshot

Weakly Supervised Localization and Learning with Generic Knowledge

inferredcites →https://doi.org/10.1007/s11263-013-0620-5openalex-snapshot

Selective Search for Object Recognition

inferredcites →https://doi.org/10.1109/cvpr.2012.6248077openalex-snapshot

Semantic segmentation using regions and parts

inferredcites →https://doi.org/10.1109/cvpr.2013.253openalex-snapshot

Unsupervised Joint Object Discovery and Segmentation in Internet Images

inferredcites →https://doi.org/10.1109/cvpr.2014.119openalex-snapshot

The Role of Context for Object Detection and Semantic Segmentation in the Wild

inferredcites →https://doi.org/10.1109/cvpr.2014.310openalex-snapshot

Generating Object Segmentation Proposals Using Global and Local Search

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.2015.7298597openalex-snapshot

Expanding object detector's Horizon: Incremental learning framework for object detection in videos

inferredcites →https://doi.org/10.1109/cvpr.2015.7298673openalex-snapshot

Adaptive region pooling for object detection

inferredcites →https://doi.org/10.1109/cvpr.2015.7298724openalex-snapshot

Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals

inferredcites →https://doi.org/10.1109/cvpr.2015.7298735openalex-snapshot

Fast action proposals for human action detection and search

inferredcites →https://doi.org/10.1109/cvpr.2015.7298765openalex-snapshot

Learning to propose objects

inferredcites →https://doi.org/10.1109/cvpr.2015.7298874openalex-snapshot

Improving object proposals with multi-thresholding straddling expansion

inferredcites →https://doi.org/10.1109/cvpr.2015.7298974openalex-snapshot

Second-order constrained parametric proposals and sequential search-based structured prediction for semantic segmentation in RGB-D images

inferredcites →https://doi.org/10.1109/cvpr.2015.7298982openalex-snapshot

Watch and learn: Semi-supervised learning of object detectors from videos

inferredcites →https://doi.org/10.1109/cvpr.2015.7298987openalex-snapshot

Semantic object segmentation via detection in weakly labeled video

inferredcites →https://doi.org/10.1109/cvpr.2015.7299012openalex-snapshot

Object proposal by multi-branch hierarchical segmentation

inferredcites →https://doi.org/10.1109/cvpr.2015.7299025openalex-snapshot

Convolutional feature masking for joint object and stuff segmentation

inferredcites →https://doi.org/10.1109/cvpr.2015.7299047openalex-snapshot

Robust video segment proposals with painless occlusion handling

inferredcites →https://doi.org/10.1109/cvpr.2015.7299063openalex-snapshot

Active learning and discovery of object categories in the presence of unnameable instances

inferredcites →https://doi.org/10.1109/cvpr.2015.7299102openalex-snapshot

segDeepM: Exploiting segmentation and context in deep neural networks for object detection

inferredcites →https://doi.org/10.1109/iccv.2011.6126351openalex-snapshot

Learning a category independent object detection cascade

inferredcites →https://doi.org/10.1109/iccv.2011.6126409openalex-snapshot

Scale and object aware image retargeting for thumbnail browsing

inferredcites →https://doi.org/10.1109/iccv.2013.10openalex-snapshot

Regionlets for Generic Object Detection

inferredcites →https://doi.org/10.1109/iccv.2013.315openalex-snapshot

Prime Object Proposals with Randomized Prim's Algorithm

inferredcites →https://doi.org/10.1109/iccv.2013.369openalex-snapshot

Segmentation Driven Object Detection with Fisher Vectors

inferredcites →https://doi.org/10.1109/iccv.2015.181openalex-snapshot

Boosting Object Proposals: From Pascal to COCO

inferredcites →https://doi.org/10.1109/iccv.2015.285openalex-snapshot

DeepBox: Learning Objectness with Convolutional Networks

inferredcites →https://doi.org/10.1109/iccv.2015.296openalex-snapshot

DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

inferredcites →https://doi.org/10.1109/iccv.2015.363openalex-snapshot

Unsupervised Object Discovery and Tracking in Video Collections

inferredcites →https://doi.org/10.1109/iccv.2015.40openalex-snapshot

Oriented Object Proposals

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.2011.158openalex-snapshot

Weakly Supervised Learning of Interactions between Humans and Objects

inferredcites →https://doi.org/10.1109/tpami.2011.231openalex-snapshot

CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts

inferredcites →https://doi.org/10.1109/tpami.2012.28openalex-snapshot

Measuring the Objectness of Image Windows

inferredcites →https://doi.org/10.1109/tpami.2013.122openalex-snapshot

Category-Independent Object Proposals with Diverse Ranking

inferredcites →https://doi.org/10.1109/tpami.2014.2315811openalex-snapshot

Data-Driven Objectness

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.2015.2465908openalex-snapshot

What Makes for Effective Detection Proposals?

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/10.48550/arxiv.1310.1531openalex-snapshot

DeCAF: A Deep Convolutional Activation Feature for Generic Visual 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.1501.04587openalex-snapshot

Transferring Rich Feature Hierarchies for Robust Visual Tracking

inferredcites →https://doi.org/10.48550/arxiv.1506.06204openalex-snapshot

Learning to Segment Object Candidates

inferredcites →https://doi.org/10.5244/c.28.24openalex-snapshot

How good are detection proposals, really?

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_11openalex-snapshot

Toward Scale-Invariance and Position-Sensitive Region Proposal Networks

inferred← citeshttps://doi.org/10.1007/978-3-030-01252-6_22openalex-snapshot

Weakly Supervised Region Proposal Network and Object Detection

inferred← citeshttps://doi.org/10.1007/978-3-030-11021-5_40openalex-snapshot

MASON: A Model AgnoStic ObjectNess Framework

inferred← citeshttps://doi.org/10.1007/978-3-030-66096-3_32openalex-snapshot

What Leads to Generalization of Object Proposals?

inferred← citeshttps://doi.org/10.1007/978-3-031-92591-7_10openalex-snapshot

Aligning Object Detector Bounding Boxes with Human Preference

inferred← citeshttps://doi.org/10.1007/978-3-319-46484-8_20openalex-snapshot

SPLeaP: Soft Pooling of Learned Parts for Image Classification

inferred← citeshttps://doi.org/10.1007/s00500-017-2696-2openalex-snapshot

Vehicle license plate detection using region-based convolutional neural networks

inferred← citeshttps://doi.org/10.1007/s10462-025-11256-0openalex-snapshot

Oriented object detection in optical remote sensing images using deep learning: a survey

inferred← citeshttps://doi.org/10.1007/s10846-017-0735-yopenalex-snapshot

Evaluation of Object Proposals and ConvNet Features for Landmark-based Visual Place Recognition

inferred← citeshttps://doi.org/10.1007/s11042-017-4561-9openalex-snapshot

Adaptive video object proposals by a context-aware model

inferred← citeshttps://doi.org/10.1007/s11042-017-5471-6openalex-snapshot

Insights of object proposal evaluation

inferred← citeshttps://doi.org/10.1007/s11263-018-1101-7openalex-snapshot

Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection

inferred← citeshttps://doi.org/10.1007/s11263-019-01247-4openalex-snapshot

Deep Learning for Generic Object Detection: A Survey

inferred← citeshttps://doi.org/10.1007/s41095-018-0120-1openalex-snapshot

BING: Binarized normed gradients for objectness estimation at 300fps

inferred← citeshttps://doi.org/10.1016/j.autcon.2021.103823openalex-snapshot

Sewer pipe defect detection via deep learning with local and global feature fusion

inferred← citeshttps://doi.org/10.1016/j.cviu.2016.05.002openalex-snapshot

Large scale Gaussian Process for overlap-based object proposal scoring

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.matpr.2021.02.533openalex-snapshot

Object detection through region proposal based techniques

inferred← citeshttps://doi.org/10.1016/j.neucom.2018.01.050openalex-snapshot

Salient object detection via proposal selection

inferred← citeshttps://doi.org/10.1088/1742-6596/1976/1/012007openalex-snapshot

Image recognition and detection based on fast area convolutional neural network

inferred← citeshttps://doi.org/10.1088/1755-1315/252/4/042050openalex-snapshot

Structured Light Detection Algorithm based on Deep Learning

inferred← citeshttps://doi.org/10.1109/access.2019.2915944openalex-snapshot

Deep Learning-Based Super-Resolution Reconstruction and Marker Detection for Drone Landing

inferred← citeshttps://doi.org/10.1109/aic57670.2023.10263964openalex-snapshot

A People Counting System Using Convolutional Neural Network for Avoiding Public Gathering and Maintaining Social Distancing

inferred← citeshttps://doi.org/10.1109/ciact.2018.8480413openalex-snapshot

Region-based Object Detection and Classification using Faster R-CNN

inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.00438openalex-snapshot

Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity

inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.01559openalex-snapshot

Point-Level Region Contrast for Object Detection Pre-Training

inferred← citeshttps://doi.org/10.1109/ecmr.2019.8870959openalex-snapshot

Learning Objectness from Sonar Images for Class-Independent Object Detection

inferred← citeshttps://doi.org/10.1109/i-smac.2018.8653762openalex-snapshot

VEHICLE LOGO RECOGNITION USING ENHANCED LEARNING : VLR Recognition

inferred← citeshttps://doi.org/10.1109/icccnt54827.2022.9984598openalex-snapshot

Pneumonia Detection using Ensemble of Modified ViT-YOLO Models

inferred← citeshttps://doi.org/10.1109/iccv.2015.137openalex-snapshot

Cascaded Sparse Spatial Bins for Efficient and Effective Generic Object Detection

inferred← citeshttps://doi.org/10.1109/iccv.2015.286openalex-snapshot

Active Object Localization with Deep Reinforcement Learning

inferred← citeshttps://doi.org/10.1109/iccv51070.2023.00371openalex-snapshot

Segment Anything

inferred← citeshttps://doi.org/10.1109/icip.2017.8296386openalex-snapshot

Do we really need more training data for object localization

inferred← citeshttps://doi.org/10.1109/icme.2017.8019391openalex-snapshot

Object trajectory proposal

inferred← citeshttps://doi.org/10.1109/ijcnn.2018.8489310openalex-snapshot

Multi ROI and Multi Map Networks for Accurate and Efficient Pedestrian Detection

inferred← citeshttps://doi.org/10.1109/iros40897.2019.8967830openalex-snapshot

Object Proposal Algorithms in the Wild: Are they Generalizable to Robot Perception?

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/paap.2018.00046openalex-snapshot

Object Proposal Generation for Unsupervised Object Localization

inferred← citeshttps://doi.org/10.1109/prrs.2016.7867018openalex-snapshot

A comprehensive study on object proposals methods for vehicle detection in aerial images

inferred← citeshttps://doi.org/10.1109/tcsvt.2017.2775446openalex-snapshot

Interactive Hierarchical Object Proposals

inferred← citeshttps://doi.org/10.1109/tpami.2015.2465908openalex-snapshot

What Makes for Effective Detection Proposals?

inferred← citeshttps://doi.org/10.1109/tpami.2016.2577031openalex-snapshot

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

inferred← citeshttps://doi.org/10.1109/tpami.2018.2810288openalex-snapshot

Mixed Supervised Object Detection with Robust Objectness Transfer

inferred← citeshttps://doi.org/10.1109/wacv57701.2024.00434openalex-snapshot

Towards Addressing the Misalignment of Object Proposal Evaluation for Vision-Language Tasks via Semantic Grounding

inferred← citeshttps://doi.org/10.1109/yac.2017.7967562openalex-snapshot

Weakly-supervised TV logo detection

inferred← citeshttps://doi.org/10.1111/mice.12425openalex-snapshot

Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization

inferred← citeshttps://doi.org/10.1117/12.2241527openalex-snapshot

Generating object proposals for improved object detection in aerial images

inferred← citeshttps://doi.org/10.1145/3318463openalex-snapshot

Harvesting Visual Objects from Internet Images via Deep-Learning-Based Objectness Assessment

inferred← citeshttps://doi.org/10.1145/3432861openalex-snapshot

Multi-peak Graph-based Multi-instance Learning for Weakly Supervised Object Detection

inferred← citeshttps://doi.org/10.1177/14759217221123485openalex-snapshot

Crack detection based on deep learning: a method for evaluating the object detection networks considering the random fractal of crack

inferred← citeshttps://doi.org/10.12677/jisp.2019.82007openalex-snapshot

Target Detection and Recognition Based on Improved Faster R-CNN

inferred← citeshttps://doi.org/10.1609/aaai.v32i1.11277openalex-snapshot

Deep Region Hashing for Generic Instance Search from Images

inferred← citeshttps://doi.org/10.20965/jrm.2020.p1200openalex-snapshot

Garbage Detection Using YOLOv3 in Nakanoshima Challenge

inferred← citeshttps://doi.org/10.32628/ijsrset23102128openalex-snapshot

Scenerio-Based Image Generation Using Deep Learning

inferred← citeshttps://doi.org/10.3390/rs11182176openalex-snapshot

Multiple-Oriented and Small Object Detection with Convolutional Neural Networks for Aerial Image

inferred← citeshttps://doi.org/10.3390/s17122720openalex-snapshot

Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks

inferred← citeshttps://doi.org/10.3390/s18061703openalex-snapshot

LightDenseYOLO: A Fast and Accurate Marker Tracker for Autonomous UAV Landing by Visible Light Camera Sensor on Drone

inferred← citeshttps://doi.org/10.48550/arxiv.1506.06204openalex-snapshot

Learning to Segment Object Candidates

inferred← citeshttps://doi.org/10.48550/arxiv.1511.06015openalex-snapshot

Active Object Localization with Deep Reinforcement Learning

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.2007.12082openalex-snapshot

A Study on Evaluation Standard for Automatic Crack Detection Regard the Random Fractal

inferred← citeshttps://doi.org/10.51583/ijltemas.2025.1407000037openalex-snapshot

A Novel Method for Using Deep Reinforcement Machine Learning to Identify Objects

inferred← citeshttps://doi.org/10.5220/0008991104520461openalex-snapshot

A New Approach Combining Trained Single-view Networks with Multi-view Constraints for Robust Multi-view Object Detection and Labelling

inferred← citeshttps://doi.org/10.5244/c.30.90openalex-snapshot

Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization

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A REVIEW OF CROWN DETECTION IN YAKSHAGANA IMAGES USING CNN ALGORITHM

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