Evidence map›Paper›PMID 32510058›Full record

ArticleBMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference2019

Unmasking the Devil in the Details: What Works for Deep Facial Action Coding?

Koichiro Niinuma, Laszlo A Jeni, Itir Onal Ertugrul, Jeffrey F Cohn

Abstract read
PubMed Full text at PMC
In one paragraph

Article in BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Synthetic Expressions are Better Than Real for Learning to Detect Facial Actions.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2021
    Article
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

4 authors.

Koichiro NiinumaFujitsu Laboratories of America Pittsburgh, PA, USA.
Laszlo A JeniRobotics Institute Carnegie Mellon University Pittsburgh, PA, USA.
Itir Onal ErtugrulRobotics Institute Carnegie Mellon University Pittsburgh, PA, USA.
Jeffrey F CohnDepartment of Psychology University of Pittsburgh Pittsburgh, PA, USA.

Funding

Measuring Automated Behavioral Observations & Vocal Expressions (ABOVE) while Recording from the BrainUH3NS100549 · NINDS · BAYLOR COLLEGE OF MEDICINE · PI GOODMAN, WAYNE K · 2016 to 2022
$7.8M
Automatic Multimodal Affect Detection for Research and Clinical Use R01MH096951 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI COHN, JEFFREY F · 2012 to 2021
$5.6M
NIMH NIH HHS R01 MH096951NINDS NIH HHS UH3 NS100549
6 · The paper itself

Abstract

The performance of automated facial expression coding has improving steadily as evidenced by results of the latest Facial Expression Recognition and Analysis (FERA 2017) Challenge. Advances in deep learning techniques have been key to this success. Yet the contribution of critical design choices remains largely unknown. Using the FERA 2017 database, we systematically evaluated design choices in pre-training, feature alignment, model size selection, and optimizer details. Our findings vary from the counter-intuitive (e.g., generic pre-training outperformed face-specific models) to best practices in tuning optimizers. Informed by what we found, we developed an architecture that exceeded state-of-the-art on FERA 2017. We achieved a 3.5% increase in F

Identifiers

PMID32510058
PMCPMC7274256

What OpenQuestion holds

Textmetadata
LicenceTDM
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.