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Fine-Grained Categorization for 3D Scene Understanding

https://doi.org/10.5244/c.26.36

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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-642-15561-1_32openalex-snapshot

Visual Recognition with Humans in the Loop

inferredcites →https://doi.org/10.1007/978-3-642-15561-1_34openalex-snapshot

Monocular 3D Scene Modeling and Inference: Understanding Multi-Object Traffic Scenes

inferredcites →https://doi.org/10.1007/978-3-642-15561-1_35openalex-snapshot

Blocks World Revisited: Image Understanding Using Qualitative Geometry and Mechanics

inferredcites →https://doi.org/10.1007/s11263-008-0137-5openalex-snapshot

Putting Objects in Perspective

inferredcites →https://doi.org/10.1007/s11263-009-0275-4openalex-snapshot

The Pascal Visual Object Classes (VOC) Challenge

inferredcites →https://doi.org/10.1016/0004-3702(81)90028-xopenalex-snapshot

Symbolic reasoning among 3-D models and 2-D images

inferredcites →https://doi.org/10.1016/0004-3702(86)90052-4openalex-snapshot

Perceptual organization and the representation of natural form

inferredcites →https://doi.org/10.1016/0004-3702(87)90070-1openalex-snapshot

Three-dimensional object recognition from single two-dimensional images

inferredcites →https://doi.org/10.1016/0010-0285(76)90013-xopenalex-snapshot

Basic objects in natural categories

inferredcites →https://doi.org/10.1037//0096-3445.113.2.169openalex-snapshot

Objects, parts, and categories.

inferredcites →https://doi.org/10.1098/rspb.1978.0020openalex-snapshot

Representation and recognition of the spatial organization of three-dimensional shapes

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

Histograms of Oriented Gradients for Human Detection

inferredcites →https://doi.org/10.1109/cvpr.2006.68openalex-snapshot

Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

inferredcites →https://doi.org/10.1109/cvpr.2010.5540018openalex-snapshot

Locality-constrained Linear Coding for image classification

inferredcites →https://doi.org/10.1109/cvpr.2011.5995368openalex-snapshot

Combining randomization and discrimination for fine-grained image categorization

inferredcites →https://doi.org/10.1109/cvpr.2011.5995448openalex-snapshot

From 3D scene geometry to human workspace

inferredcites →https://doi.org/10.1109/cvpr.2011.5995462openalex-snapshot

Semantic structure from motion

inferredcites →https://doi.org/10.1109/cvpr.2011.5995631openalex-snapshot

Action recognition from a distributed representation of pose and appearance

inferredcites →https://doi.org/10.1109/cvpr.2012.6248075openalex-snapshot

Teaching 3D geometry to deformable part models

inferredcites →https://doi.org/10.1109/iccv.2007.4408987openalex-snapshot

3D generic object categorization, localization and pose estimation

inferredcites →https://doi.org/10.1109/iccv.2011.6126238openalex-snapshot

Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance

inferredcites →https://doi.org/10.1109/iccv.2011.6126383openalex-snapshot

Scene recognition and weakly supervised object localization with deformable part-based models

inferredcites →https://doi.org/10.1109/iccv.2011.6126539openalex-snapshot

Multiclass recognition and part localization with humans in the loop

inferredcites →https://doi.org/10.1109/iccvw.2011.6130294openalex-snapshot

Revisiting 3D geometric models for accurate object shape and pose

inferredcites →https://doi.org/10.1109/icvgip.2008.47openalex-snapshot

Automated Flower Classification over a Large Number of Classes

inferredcites →https://doi.org/10.1109/tpami.2006.79openalex-snapshot

One-shot learning of object categories

inferredcites →https://doi.org/10.1109/tpami.2009.167openalex-snapshot

Object Detection with Discriminatively Trained Part-Based Models

inferredcites →https://doi.org/10.1177/0278364911400640openalex-snapshot

Ford Campus vision and lidar data set

inferredcites →https://doi.org/10.1184/r1/6475985openalex-snapshot

Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification

inferredcites →https://doi.org/10.7551/mitpress/7503.003.0014openalex-snapshot

Subordinate class recognition using relational object models

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.4789297openalex-extracted

Pceve: Part Contribution Evaluation Based Model Explanation for Human Figure Drawing Assessment and Beyond

inferred← citeshttps://doi.org/10.1007/978-3-030-60633-6_33openalex-snapshot

TriSpaSurf: A Triple-View Outline Guided 3D Surface Reconstruction of Vehicles from Sparse Point Cloud

inferred← citeshttps://doi.org/10.1007/978-3-319-10590-1_54openalex-snapshot

Part-Based R-CNNs for Fine-Grained Category Detection

inferred← citeshttps://doi.org/10.1007/978-3-319-10593-2_31openalex-snapshot

Jointly Optimizing 3D Model Fitting and Fine-Grained Classification

inferred← citeshttps://doi.org/10.1007/s11042-019-08265-xopenalex-snapshot

What is damaged: a benchmark dataset for abnormal traffic object classification

inferred← citeshttps://doi.org/10.1007/s11042-020-08666-3openalex-snapshot

Adversarial erasing attention for fine-grained image classification

inferred← citeshttps://doi.org/10.1007/s11263-014-0698-4openalex-snapshot

The Ignorant Led by the Blind: A Hybrid Human–Machine Vision System for Fine-Grained Categorization

inferred← citeshttps://doi.org/10.1007/s11265-020-01567-6openalex-snapshot

Vehicle Attribute Recognition by Appearance: Computer Vision Methods for Vehicle Type, Make and Model Classification

inferred← citeshttps://doi.org/10.1007/s11390-018-1822-7openalex-snapshot

A Novel Fine-Grained Method for Vehicle Type Recognition Based on the Locally Enhanced PCANet Neural Network

inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2016.02.007openalex-snapshot

Street-side vehicle detection, classification and change detection using mobile laser scanning data

inferred← citeshttps://doi.org/10.1016/j.jvcir.2019.05.002openalex-snapshot

Hierarchical deep transfer learning for fine-grained categorization on micro datasets

inferred← citeshttps://doi.org/10.1016/j.measurement.2020.107801openalex-snapshot

Vehicle weight identification system for spatiotemporal load distribution on bridges based on non-contact machine vision technology and deep learning algorithms

inferred← citeshttps://doi.org/10.1016/j.neucom.2020.07.147openalex-snapshot

Multi-level dictionary learning for fine-grained images categorization with attention model

inferred← citeshttps://doi.org/10.1016/j.patrec.2017.10.020openalex-snapshot

Convolutional low-resolution fine-grained classification

inferred← citeshttps://doi.org/10.1016/j.patrec.2021.11.015openalex-snapshot

Category attention transfer for efficient fine-grained visual categorization

inferred← citeshttps://doi.org/10.1109/access.2021.3118786openalex-snapshot

Adversarial Networks With Circular Attention Mechanism for Fine-Grained Domain Adaptation

inferred← citeshttps://doi.org/10.1109/crv.2018.00024openalex-snapshot

A Hierarchical Deep Architecture and Mini-batch Selection Method for Joint Traffic Sign and Light Detection

inferred← citeshttps://doi.org/10.1109/cvpr.2013.422openalex-snapshot

Occlusion Patterns for Object Class Detection

inferred← citeshttps://doi.org/10.1109/cvpr.2014.470openalex-snapshot

Are Cars Just 3D Boxes? Jointly Estimating the 3D Shape of Multiple Objects

inferred← citeshttps://doi.org/10.1109/cvpr.2015.7298639openalex-snapshot

A coarse-to-fine model for 3D pose estimation and sub-category recognition

inferred← citeshttps://doi.org/10.1109/cvpr.2015.7299187openalex-snapshot

Fine-grained classification of pedestrians in video: Benchmark and state of the art

inferred← citeshttps://doi.org/10.1109/cvpr.2015.7299194openalex-snapshot

Fine-grained recognition without part annotations

inferred← citeshttps://doi.org/10.1109/cvpr.2016.127openalex-snapshot

Fine-Grained Image Classification by Exploring Bipartite-Graph Labels

inferred← citeshttps://doi.org/10.1109/cvpr.2016.131openalex-snapshot

Mining Discriminative Triplets of Patches for Fine-Grained Classification

inferred← citeshttps://doi.org/10.1109/cvpr.2016.132openalex-snapshot

Part-Stacked CNN for Fine-Grained Visual Categorization

inferred← citeshttps://doi.org/10.1109/cvpr.2016.328openalex-snapshot

BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition

inferred← citeshttps://doi.org/10.1109/cvpr.2016.413openalex-snapshot

Recognizing Car Fluents from Video

inferred← citeshttps://doi.org/10.1109/cvpr42600.2020.00923openalex-snapshot

Progressive Adversarial Networks for Fine-Grained Domain Adaptation

inferred← citeshttps://doi.org/10.1109/dicta.2015.7371318openalex-snapshot

Unsupervised Processing of Vehicle Appearance for Automatic Understanding in Traffic Surveillance

inferred← citeshttps://doi.org/10.1109/dicta.2018.8615855openalex-snapshot

Fine-Grained Categorization by Deep Part-Collaboration Convolution Net

inferred← citeshttps://doi.org/10.1109/icassp40776.2020.9052902openalex-snapshot

Global Structure Graph Guided Fine-Grained Vehicle Recognition

inferred← citeshttps://doi.org/10.1109/iccse.2019.8845061openalex-snapshot

Research on Fine-Grain Model Recognition Based on Branch Feedback Convolution Neural Network

inferred← citeshttps://doi.org/10.1109/iccv.2013.217openalex-snapshot

Predicting an Object Location Using a Global Image Representation

inferred← citeshttps://doi.org/10.1109/iccv.2013.96openalex-snapshot

Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction

inferred← citeshttps://doi.org/10.1109/iccv.2017.207openalex-snapshot

See the Glass Half Full: Reasoning About Liquid Containers, Their Volume and Content

inferred← citeshttps://doi.org/10.1109/iccvw.2013.77openalex-snapshot

3D Object Representations for Fine-Grained Categorization

inferred← citeshttps://doi.org/10.1109/icip.2015.7350898openalex-snapshot

Exploiting effects of parts in fine-grained categorization of vehicles

inferred← citeshttps://doi.org/10.1109/icme.2017.8019386openalex-snapshot

Learning deep and sparse feature representation for fine-grained object recognition

inferred← citeshttps://doi.org/10.1109/icme.2018.8486443openalex-snapshot

FF-CMnet: A CNN-Based Model for Fine-Grained Classification of Car Models Based on Feature Fusion

inferred← citeshttps://doi.org/10.1109/icpr.2014.15openalex-snapshot

Learning Features and Parts for Fine-Grained Recognition

inferred← citeshttps://doi.org/10.1109/icsp.2018.8652320openalex-snapshot

Learning Two-level Features for Fine-grained Image Classification

inferred← citeshttps://doi.org/10.1109/tip.2016.2621661openalex-snapshot

Friend or Foe: Fine-Grained Categorization With Weak Supervision

inferred← citeshttps://doi.org/10.1109/tits.2018.2799228openalex-snapshot

BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance

inferred← citeshttps://doi.org/10.1109/tits.2020.3027451openalex-snapshot

A Large-Scale Frontal Vehicle Image Dataset for Fine-Grained Vehicle Categorization

inferred← citeshttps://doi.org/10.1109/tpami.2015.2408347openalex-snapshot

Multi-View and 3D Deformable Part Models

inferred← citeshttps://doi.org/10.1109/tpami.2015.2509988openalex-snapshot

Data-Driven Detection of Prominent Objects

inferred← citeshttps://doi.org/10.1109/wacv.2014.6836081openalex-snapshot

Attribute-based vehicle recognition using viewpoint-aware multiple instance SVMs

inferred← citeshttps://doi.org/10.1145/2808492.2808495openalex-snapshot

Vehicle detection and classification based on convolutional neural network

inferred← citeshttps://doi.org/10.1364/fio.2021.jth5a.46openalex-snapshot

Features Extraction and Classification Methods for Automatic Selection of Two Different Types of Paint for Automotive Parts

inferred← citeshttps://doi.org/10.18297/etd/2630openalex-snapshot

Vehicle make and model recognition for intelligent transportation monitoring and surveillance.

inferred← citeshttps://doi.org/10.2139/ssrn.5174011openalex-snapshot

Pceve: Part Contribution Evaluation Based Model Explanation for Human Figure Drawing Assessment and Beyond

inferred← citeshttps://doi.org/10.48550/arxiv.1312.6095openalex-snapshot

Multi-View Priors for Learning Detectors from Sparse Viewpoint Data

inferred← citeshttps://doi.org/10.48550/arxiv.1406.2952openalex-snapshot

Bird Species Categorization Using Pose Normalized Deep Convolutional Nets

inferred← citeshttps://doi.org/10.48550/arxiv.1511.07063openalex-snapshot

Fine-grained pose prediction, normalization, and recognition

inferred← citeshttps://doi.org/10.48550/arxiv.1610.00824openalex-snapshot

Real Time Fine-Grained Categorization with Accuracy and Interpretability

inferred← citeshttps://doi.org/10.5244/c.28.53openalex-snapshot

3D Pose-by-Detection of Vehicles via Discriminatively Reduced Ensembles of Correlation Filters

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