Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00358883" target="_blank" >RIV/68407700:21730/22:00358883 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/CVPR52688.2022.01357" target="_blank" >https://doi.org/10.1109/CVPR52688.2022.01357</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPR52688.2022.01357" target="_blank" >10.1109/CVPR52688.2022.01357</a>

Result language

angličtina

Original language name

Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos

Original language description

Human actions often induce changes of object states such as “cutting an apple”, “cleaning shoes” or “pouring coffee”. In this paper, we seek to temporally localize ob ject states (e.g. “empty” and “full” cup) together with the corresponding state-modifying actions (“pouring coffee”) in long uncurated videos with minimal supervision. The contributions of this work are threefold. First, we develop a self-supervised model for jointly learning state-modifying actions together with the corresponding object states from an uncurated set of videos from the Internet. The model is self-supervised by the causal ordering signal, i.e. initial ob ject state -> manipulating action -> end state. Second, to cope with noisy uncurated training data, our model incor porates a noise adaptive weighting module supervised by a small number of annotated still images, that allows to ef ficiently filter out irrelevant videos during training. Third, we collect a new dataset with more than 2600 hours of video and 34 thousand changes of object states, and manually an notate a part of this data to validate our approach. Our re sults demonstrate substantial improvements over prior work in both action and object state-recognition in video.

Czech name

—

Czech description

—

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/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>

Continuities

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

Publication year

2022

Confidentiality

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

Article name in the collection

Proceeding 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

ISBN

978-1-6654-6946-3

ISSN

1063-6919

e-ISSN

2575-7075

Number of pages

11

Pages from-to

13936-13946

Publisher name

IEEE

Place of publication

Piscataway

Event location

New Orleans, Louisiana

Event date

Jun 19, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000870759107004