Deep Contextual Attention for Human-Object Interaction Detection

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

Organisaatiot

  • Inception Institute of Artificial Intelligence
  • Tianjin University

Kuvaus

This work proposes to combine neural networks with the compositional hierarchy of human bodies for efficient and complete human parsing. We formulate the approach as a neural information fusion framework. Our model assembles the information from three inference processes over the hierarchy: direct inference (directly predicting each part of a human body using image information), bottom-up inference (assembling knowledge from constituent parts), and top-down inference (leveraging context from parent nodes). The bottom-up and top-down inferences explicitly model the compositional and decompositional relations in human bodies, respectively. In addition, the fusion of multi-source information is conditioned on the inputs, i.e., by estimating and considering the confidence of the sources. The whole model is end-to-end differentiable, explicitly modeling information flows and structures. Our approach is extensively evaluated on four popular datasets, outperforming the state-of-the-arts in all cases, with a fast processing speed of 23fps. Our code and results have been released to help ease future research in this direction.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoProceedings of the International Conference on Computer Vision (ICCV2019)
TilaHyväksytty/In press - lokakuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Conference on Computer Vision - Seoul, Etelä-Korea
Kesto: 27 lokakuuta 20192 marraskuuta 2019
http://iccv2019.thecvf.com/

Conference

ConferenceIEEE International Conference on Computer Vision
LyhennettäICCV
MaaEtelä-Korea
KaupunkiSeoul
Ajanjakso27/10/201902/11/2019
www-osoite

ID: 40364287