Evidence map›Paper›PMID 31930192›Full record

ArticleFrontiers in computer science2019

D-PAttNet: Dynamic Patch-Attentive Deep Network for Action Unit Detection.

Itir Onal Ertugrul, Le Yang, László A Jeni, Jeffrey F Cohn

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Article in Frontiers in computer science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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.

Itir Onal ErtugrulRobotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States.
Le YangSchool of Computer Science, Northwestern Polytechnical University, Xian, China.
László A JeniRobotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States.
Jeffrey F CohnDepartment of Psychology, University of Pittsburgh, Pittsburgh, PA, United States.

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

Facial action units (AUs) relate to specific local facial regions. Recent efforts in automated AU detection have focused on learning the facial patch representations to detect specific AUs. These efforts have encountered three hurdles. First, they implicitly assume that facial patches are robust to head rotation; yet non-frontal rotation is common. Second, mappings between AUs and patches are defined a priori, which ignores co-occurrences among AUs. And third, the dynamics of AUs are either ignored or modeled sequentially rather than simultaneously as in human perception. Inspired by recent advances in human perception, we propose a dynamic patch-attentive deep network, called D-PAttNet, for AU detection that (i) controls for 3D head and face rotation, (ii) learns mappings of patches to AUs, and (iii) models spatiotemporal dynamics. D-PAttNet approach significantly improves upon existing state of the art.

Indexed as

3D-CNN3D face registrationaction unit detectionpatch-basedsigmoidal attention

Identifiers

PMID31930192
PMCPMC6953909

What OpenQuestion holds

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Registered trials

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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.