Rethinking matching-based few-shot action recognition

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00370588" target="_blank" >RIV/68407700:21230/23:00370588 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-31435-3_15" target="_blank" >https://doi.org/10.1007/978-3-031-31435-3_15</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-31435-3_15" target="_blank" >10.1007/978-3-031-31435-3_15</a>

Result language

angličtina

Original language name

Rethinking matching-based few-shot action recognition

Original language description

Few-shot action recognition, i.e. recognizing new action classes given only a few examples, benefits from incorporating temporal information. Prior work either encodes such information in the representation itself and learns classifiers at test time, or obtains frame-level features and performs pairwise temporal matching. We first evaluate a number of matching-based approaches using features from spatio-temporal back- bones, a comparison missing from the literature, and show that the gap in performance between simple baselines and more complicated methods is significantly reduced. Inspired by this, we propose Chamfer++, a non-temporal matching function that achieves state-of-the-art results in few-shot action recognition. We show that, when starting from temporal features, our parameter-free and interpretable approach can outperform all other matching-based and classifier methods for one-shot action recognition on three common datasets without using temporal information in the matching stage. Project page: https://jbertrand89.github.io/matching-based-fsar

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GM21-28830M" target="_blank" >GM21-28830M: Learning Universal Visual Representation with Limited Supervision</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

SCIA 2023: Image Analysis, Part I

ISBN

978-3-031-31434-6

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

22

Pages from-to

215-236

Publisher name

Springer

Place of publication

Cham

Event location

Sirkka

Event date

Apr 18, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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