From Forest to Zoo: Great Ape Behavior Recognition with ChimpBehave.

Michael Fuchs, Emilie Genty, Adrian Bangerter, Klaus Zuberbühler, Jean-Marc Odobez, Paul Cotofrei

Abstract read

In one paragraph

Article in International journal of computer vision, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

The Effects of Light on Vertebrate Welfare: A Review.Animals : an open access journal from MDPI · 2025
Review

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Michael FuchsInstitute of Information Management, University of Neuchâtel, Neuchâtel, Switzerland.ORCID 0000-0002-8937-1427

Emilie GentyInstitute of Biology, University of Neuchâtel, Neuchâtel, Switzerland.ORCID https://orcid.org/0000-0003-2461-2442

Adrian BangerterInstitute of Work and Organizational Psychology, University of Neuchâtel, Neuchâtel, Switzerland.ORCID https://orcid.org/0000-0001-6989-8654

Klaus ZuberbühlerInstitute of Biology, University of Neuchâtel, Neuchâtel, Switzerland.ORCID https://orcid.org/0000-0001-8378-088X

Jean-Marc OdobezPerception and Activity Understanding Group, Idiap Research Institute, Martigny, Switzerland.ORCID https://orcid.org/0000-0002-9537-9898

Paul CotofreiInstitute of Information Management, University of Neuchâtel, Neuchâtel, Switzerland.ORCID https://orcid.org/0000-0002-4103-5467

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper addresses the significant challenge of recognizing behaviors in non-human primates, specifically focusing on chimpanzees. Automated behavior recognition is crucial for both conservation efforts and the advancement of behavioral research. However, it is often hindered by the labor-intensive process of manual video annotation. Despite the availability of large-scale animal behavior datasets, effectively applying machine learning models across varied environmental settings remains a critical challenge due to the variability in data collection contexts and the specificity of annotations. In this paper, we introduce

Indexed as

Animal behavior recognitionChimpanzeesGreat apesNon-human primatesPose estimationSkeleton-based action recognitionVideo foundation models

Identifiers

PMID41078976

DOI10.1007/s11263-025-02484-6

PMCPMC12513923

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.