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Complementary Feature-Guided Hybrid Attention Prototypical Network for Few-Shot Video Classification

https://doi.org/10.2139/ssrn.4644183
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inferredcites →https://doi.org/10.1007/978-3-030-01234-2_46openalex-extracted
Compound Memory Networks for Few-Shot Video Classification
inferredcites →https://doi.org/10.1007/978-3-030-58558-7_31openalex-extracted
Few-Shot Action Recognition with Permutation-Invariant Attention
inferredcites →https://doi.org/10.1007/978-3-030-58604-1_15openalex-extracted
Attentive Prototype Few-Shot Learning with Capsule Network-Based Embedding
inferredcites →https://doi.org/10.1007/978-3-319-46484-8_2openalex-extracted
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
inferredcites →https://doi.org/10.1007/978-3-642-33374-3_41openalex-extracted
HMDB51: A Large Video Database for Human Motion Recognition
inferredcites →https://doi.org/10.1016/j.neunet.2022.11.009openalex-extracted
DANet: Semi-supervised differentiated auxiliaries guided network for video action recognition
inferredcites →https://doi.org/10.1016/j.neunet.2023.08.019openalex-extracted
Learning dynamic spatial-temporal regularized correlation filter tracking with response deviation suppression via multi-feature fusion
inferredcites →https://doi.org/10.1109/cvpr.2018.00131openalex-extracted
Learning to Compare: Relation Network for Few-Shot Learning
inferredcites →https://doi.org/10.1109/cvpr.2018.00813openalex-extracted
Non-local Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2019.00010openalex-extracted
Edge-Labeling Graph Neural Network for Few-Shot Learning
inferredcites →https://doi.org/10.1109/cvpr.2019.00049openalex-extracted
Meta-Transfer Learning for Few-Shot Learning
inferredcites →https://doi.org/10.1109/cvpr.2019.00743openalex-extracted
Revisiting Local Descriptor Based Image-To-Class Measure for Few-Shot Learning
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00067openalex-extracted
Temporal Pyramid Network for Action Recognition
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00883openalex-extracted
Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01063openalex-extracted
Few-Shot Video Classification via Temporal Alignment
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00054openalex-extracted
Temporal-Relational CrossTransformers for Few-Shot Action Recognition
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01932openalex-extracted
Hybrid Relation Guided Set Matching for Few-shot Action Recognition
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01933openalex-extracted
Spatio-temporal Relation Modeling for Few-shot Action Recognition
inferredcites →https://doi.org/10.1109/iccv.2011.6126543openalex-extracted
HMDB: A large video database for human motion recognition
inferredcites →https://doi.org/10.1109/iccv.2019.00929openalex-extracted
PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment
inferredcites →https://doi.org/10.1109/iccvw.2019.00166openalex-extracted
ProtoGAN: Towards Few Shot Learning for Action Recognition
inferredcites →https://doi.org/10.1109/tcsvt.2019.2914137openalex-extracted
Action Recognition Scheme Based on Skeleton Representation With DS-LSTM Network
inferredcites →https://doi.org/10.1109/tgrs.2021.3099033openalex-extracted
SPNet: Siamese-Prototype Network for Few-Shot Remote Sensing Image Scene Classification
inferredcites →https://doi.org/10.1109/tgrs.2022.3219726openalex-extracted
Improving Few-Shot Remote Sensing Scene Classification With Class Name Semantics
inferredcites →https://doi.org/10.1109/tpami.2019.2928540openalex-extracted
Coherence Constrained Graph LSTM for Group Activity Recognition
inferredcites →https://doi.org/10.1109/tpami.2020.3007511openalex-extracted
Label Independent Memory for Semi-Supervised Few-shot Video Classification
inferredcites →https://doi.org/10.1109/tpami.2021.3050918openalex-extracted
Spatiotemporal Co-Attention Recurrent Neural Networks for Human-Skeleton Motion Prediction
inferredcites →https://doi.org/10.1109/wacv.2018.00047openalex-extracted
A Generative Approach to Zero-Shot and Few-Shot Action Recognition
inferredcites →https://doi.org/10.1145/3386252openalex-extracted
Generalizing from a Few Examples
inferredcites →https://doi.org/10.1145/3394171.3413502openalex-extracted
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition
inferredcites →https://doi.org/10.1609/aaai.v36i2.20029openalex-extracted
TA2N: Two-Stage Action Alignment Network for Few-Shot Action Recognition
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-extracted
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
inferredcites →https://doi.org/10.48550/arxiv.1703.03400openalex-extracted
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
inferredcites →https://doi.org/10.48550/arxiv.1703.05175openalex-extracted
Prototypical Networks for Few-shot Learning
inferredcites →https://doi.org/10.48550/arxiv.1812.03664openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1903.12290openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1904.05046openalex-extracted
Generalizing from a Few Examples: A Survey on Few-Shot Learning
inferredcites →https://doi.org/10.48550/arxiv.1906.11415openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1908.06391openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1909.09157openalex-extracted
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
inferredcites →https://doi.org/10.48550/arxiv.2001.03905openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2007.11498openalex-extracted
CrossTransformers: spatially-aware few-shot transfer
inferredcites →https://doi.org/10.48550/arxiv.2101.08085openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2112.05132openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2204.13423openalex-extracted
inferredcites →https://doi.org/10.5244/c.35.94openalex-snapshot
Few-shot Action Recognition with Prototype-centered Attentive Learning
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