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Deep Architectures and Ensembles for Semantic Video Classification

https://doi.org/10.1109/tcsvt.2018.2881842
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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-319-46448-0_1openalex-snapshot
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
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Bagging predictors
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Mixture of experts: a literature survey
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An analysis of diversity measures
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1023/a:1022859003006openalex-snapshot
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
inferredcites →https://doi.org/10.1023/b:visi.0000029664.99615.94openalex-snapshot
Distinctive Image Features from Scale-Invariant Keypoints
inferredcites →https://doi.org/10.1109/34.58871openalex-snapshot
Neural network ensembles
inferredcites →https://doi.org/10.1109/cvpr.2007.383266openalex-snapshot
Fisher Kernels on Visual Vocabularies for Image Categorization
inferredcites →https://doi.org/10.1109/cvpr.2011.5995407openalex-snapshot
Action recognition by dense trajectories
inferredcites →https://doi.org/10.1109/cvpr.2014.223openalex-snapshot
Large-Scale Video Classification with Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2014.83openalex-snapshot
Multi-view Super Vector for Action Recognition
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2015.7299101openalex-snapshot
Beyond short snippets: Deep networks for video classification
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/iccv.2011.6126543openalex-snapshot
HMDB: A large video database for human motion recognition
inferredcites →https://doi.org/10.1109/iccv.2013.441openalex-snapshot
Action Recognition with Improved Trajectories
inferredcites →https://doi.org/10.1109/msp.2017.2749125openalex-snapshot
Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey
inferredcites →https://doi.org/10.1109/tcsvt.2017.2682196openalex-snapshot
Pooling the Convolutional Layers in Deep ConvNets for Video Action Recognition
inferredcites →https://doi.org/10.1109/tcsvt.2017.2706264openalex-snapshot
A Unified Metric Learning-Based Framework for Co-Saliency Detection
inferredcites →https://doi.org/10.1109/tcsvt.2017.2719043openalex-snapshot
Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition
inferredcites →https://doi.org/10.1109/tip.2017.2760512openalex-snapshot
Duplex Metric Learning for Image Set Classification
inferredcites →https://doi.org/10.1109/tip.2018.2846664openalex-snapshot
Sequential Video VLAD: Training the Aggregation Locally and Temporally
inferredcites →https://doi.org/10.1109/tkde.2010.26openalex-snapshot
Multiobjective Neural Network Ensembles Based on Regularized Negative Correlation Learning
inferredcites →https://doi.org/10.1109/tpami.2016.2558148openalex-snapshot
Rank Pooling for Action Recognition
inferredcites →https://doi.org/10.1145/2964284.2964328openalex-snapshot
Multi-Stream Multi-Class Fusion of Deep Networks for Video Classification
inferredcites →https://doi.org/10.1162/089976600300015015openalex-snapshot
Learning to Forget: Continual Prediction with LSTM
inferredcites →https://doi.org/10.3115/v1/d14-1179openalex-snapshot
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
inferredcites →https://doi.org/10.48550/arxiv.1212.0402openalex-snapshot
UCF-101: A dataset of 101 human actions classes from videos in the wild
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.1511.06314openalex-snapshot
Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks
inferredcites →https://doi.org/10.48550/arxiv.1609.08675openalex-snapshot
YouTube-8M: A Large-Scale Video Classification Benchmark
inferredcites →https://doi.org/10.48550/arxiv.1706.06905openalex-snapshot
Learnable pooling with Context Gating for video classification
inferredcites →https://doi.org/10.48550/arxiv.1707.04272openalex-snapshot
Cultivating DNN Diversity for Large Scale Video Labelling
inferredcites →https://doi.org/10.7551/mitpress/8291.001.0001openalex-snapshot
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What points at this work

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

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Attend-Fusion: Efficient Audio-Visual Fusion for Video Classification
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Visual geo-localization and attitude estimation using satellite imagery and topographical elevation for unmanned aerial vehicles
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.126064openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.image.2022.116917openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.knosys.2023.110993openalex-snapshot
Transformer-based descriptors with fine-grained region supervisions for visual place recognition
inferred← citeshttps://doi.org/10.1109/cacml55074.2022.00131openalex-snapshot
SE-VPR: Semantic Enhanced VPR Approach for Visual Localization
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.01392openalex-snapshot
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition
inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.01338openalex-snapshot
Dual Task Learning by Leveraging Both Dense Correspondence and Mis-Correspondence for Robust Change Detection With Imperfect Matches
inferred← citeshttps://doi.org/10.1109/icra48891.2023.10160994openalex-snapshot
GRM: Gradient Rectification Module for Visual Place Retrieval
inferred← citeshttps://doi.org/10.1109/tcsvt.2020.3045659openalex-snapshot
CNN-Based Classification of Degraded Images With Awareness of Degradation Levels
inferred← citeshttps://doi.org/10.1109/tcsvt.2022.3212434openalex-snapshot
Hybrid CNN-Transformer Features for Visual Place Recognition
inferred← citeshttps://doi.org/10.1109/tcsvt.2023.3337538openalex-snapshot
Toward Compact and Robust Model Learning Under Dynamically Perturbed Environments
inferred← citeshttps://doi.org/10.1109/tcsvt.2024.3399311openalex-snapshot
Learning to Generate Diverse Data From a Temporal Perspective for Data-Free Quantization
inferred← citeshttps://doi.org/10.1109/tim.2022.3219466openalex-snapshot
Salient-VPR: Salient Weighted Global Descriptor for Visual Place Recognition
inferred← citeshttps://doi.org/10.1109/tits.2022.3229364openalex-snapshot
Loop Closure Detection With Bidirectional Manifold Representation Consensus
inferred← citeshttps://doi.org/10.1109/wacv56688.2023.00562openalex-snapshot
ETR: An Efficient Transformer for Re-ranking in Visual Place Recognition
inferred← citeshttps://doi.org/10.1109/wacv57701.2024.00728openalex-snapshot
Self-Supervised Learning for Place Representation Generalization across Appearance Changes
inferred← citeshttps://doi.org/10.32628/cseit2062134openalex-snapshot
Video Classification Using Deep Learning
inferred← citeshttps://doi.org/10.3390/electronics14071418openalex-snapshot
GDCPlace: Geographic Distance Consistent Loss for Visual Place Recognition
inferred← citeshttps://doi.org/10.3390/s24030855openalex-snapshot
Contextual Patch-NetVLAD: Context-Aware Patch Feature Descriptor and Patch Matching Mechanism for Visual Place Recognition
inferred← citeshttps://doi.org/10.48550/arxiv.1910.11631openalex-snapshot
Learning to Localize Temporal Events in Large-scale Video Data
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MCVL: Multi-directional Camera-based Visual Localization Resistant to Seasonal and Illumination Changes
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