Few Shot Video Classification Via Temporal Alignment

June 5, 2026
Media Summary: Authors: Kaidi Cao, Jingwei Ji, Zhangjie Cao, Chien-Yi Chang, Juan Carlos Niebles Description: Difficulty in collecting and ... ECCV2020 Workshop on Imbalance Problems in Computer Vision (IPCV) contributed paper titled "Generalized Many-Way ... Authors: Muhammad Waleed Gondal; Jochen Gast; Inigo Alonso Ruiz; Richard Droste; Tommaso Macri; Suren Kumar; Luitpold ...

Few Shot Video Classification Via Temporal Alignment - Detailed Analysis & Overview

Authors: Kaidi Cao, Jingwei Ji, Zhangjie Cao, Chien-Yi Chang, Juan Carlos Niebles Description: Difficulty in collecting and ... ECCV2020 Workshop on Imbalance Problems in Computer Vision (IPCV) contributed paper titled "Generalized Many-Way ... Authors: Muhammad Waleed Gondal; Jochen Gast; Inigo Alonso Ruiz; Richard Droste; Tommaso Macri; Suren Kumar; Luitpold ... Presented at CVPR 2021 Paper can be found at Code is available at ... Authors: Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood, Leonid Sigal Description: paper: arxiv.org/abs/2203.15712 code: github.com/dahyun-kang/ifsl project homepage: cvlab.postech.ac.kr/research/iFSL author's ...

Want to play with the technology yourself? Explore our interactive demo → Learn more about the ... We present a self-supervised approach for learning Authors: Biagio Brattoli, Joseph Tighe, Fedor Zhdanov, Pietro Perona, Krzysztof Chalupka Description: Trained on large datasets, ... The assumption of having a large well-labeled training set is not always realistic. How do we learn from VERY

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Few-Shot Video Classification via Temporal Alignment
IPCV Paper 04 - "Generalized Many-Way Few-Shot Video Classification"
CVPR 2020 - Few Shot Video Classification via Temporal Alignment
Domain Aligned CLIP for Few-Shot Classification
SetFit: Few Shot Learning for Text Classification
Few-Shot Classification with Feature Map Reconstruction Networks [CVPR21]
Task Discrepancy Maximization for Fine Grained Few Shot Classification | CVPR 2022
Improved Few-Shot Visual Classification
TAEN Temporal aware embedding network for few shot action recognition
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation (CVPR 2021)
Few Shot Learning - EXPLAINED!
[CVPR'22 Oral] Temporal Alignment Networks for Long-term Video

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Few-Shot Video Classification via Temporal Alignment

Few-Shot Video Classification via Temporal Alignment

Authors: Kaidi Cao, Jingwei Ji, Zhangjie Cao, Chien-Yi Chang, Juan Carlos Niebles Description: Difficulty in collecting and ...

IPCV Paper 04 - "Generalized Many-Way Few-Shot Video Classification"

IPCV Paper 04 - "Generalized Many-Way Few-Shot Video Classification"

ECCV2020 Workshop on Imbalance Problems in Computer Vision (IPCV) contributed paper titled "Generalized Many-Way ...

CVPR 2020 - Few Shot Video Classification via Temporal Alignment

CVPR 2020 - Few Shot Video Classification via Temporal Alignment

Paper: ...

Domain Aligned CLIP for Few-Shot Classification

Domain Aligned CLIP for Few-Shot Classification

Authors: Muhammad Waleed Gondal; Jochen Gast; Inigo Alonso Ruiz; Richard Droste; Tommaso Macri; Suren Kumar; Luitpold ...

SetFit: Few Shot Learning for Text Classification

SetFit: Few Shot Learning for Text Classification

SetFit:

Few-Shot Classification with Feature Map Reconstruction Networks [CVPR21]

Few-Shot Classification with Feature Map Reconstruction Networks [CVPR21]

Presented at CVPR 2021 Paper can be found at https://arxiv.org/abs/2012.01506 Code is available at ...

Task Discrepancy Maximization for Fine Grained Few Shot Classification | CVPR 2022

Task Discrepancy Maximization for Fine Grained Few Shot Classification | CVPR 2022

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Improved Few-Shot Visual Classification

Improved Few-Shot Visual Classification

Authors: Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood, Leonid Sigal Description:

TAEN Temporal aware embedding network for few shot action recognition

TAEN Temporal aware embedding network for few shot action recognition

... set our task is

Adaptive Prototype Learning and Allocation for Few-Shot Segmentation (CVPR 2021)

Adaptive Prototype Learning and Allocation for Few-Shot Segmentation (CVPR 2021)

This

Few Shot Learning - EXPLAINED!

Few Shot Learning - EXPLAINED!

Follow me on M E D I U M: https://towardsdatascience.com/likelihood-probability-and-the-math-you-should-know-9bf66db5241b ...

[CVPR'22 Oral] Temporal Alignment Networks for Long-term Video

[CVPR'22 Oral] Temporal Alignment Networks for Long-term Video

Project page: https://www.robots.ox.ac.uk/~vgg/research/tan/ 5-minute overview for "

Representation Learning via Global Temporal Alignment and Cycle-Consistency

Representation Learning via Global Temporal Alignment and Cycle-Consistency

This

[CVPR'22] Integrative Few-Shot Learning for Classification and Segmentation

[CVPR'22] Integrative Few-Shot Learning for Classification and Segmentation

paper: arxiv.org/abs/2203.15712 code: github.com/dahyun-kang/ifsl project homepage: cvlab.postech.ac.kr/research/iFSL author's ...

What is Zero-Shot Learning?

What is Zero-Shot Learning?

Want to play with the technology yourself? Explore our interactive demo → https://ibm.biz/BdKkPk Learn more about the ...

Learning by Aligning Videos in Time (CVPR 2021)

Learning by Aligning Videos in Time (CVPR 2021)

We present a self-supervised approach for learning

Rethinking Zero-Shot Video Classification: End-to-End Training for Realistic Applications

Rethinking Zero-Shot Video Classification: End-to-End Training for Realistic Applications

Authors: Biagio Brattoli, Joseph Tighe, Fedor Zhdanov, Pietro Perona, Krzysztof Chalupka Description: Trained on large datasets, ...

Progressive Temporal Feature Alignment Network for Video Inpainting

Progressive Temporal Feature Alignment Network for Video Inpainting

This paper is accepted in CVPR2021.

[ICCV 2021] Few-Shot Image Classification: a Library of Feature Extractors + a Simple Classifier

[ICCV 2021] Few-Shot Image Classification: a Library of Feature Extractors + a Simple Classifier

The assumption of having a large well-labeled training set is not always realistic. How do we learn from VERY