Detecting and Recognizing Human-Object Interactions
ABCD PBi
Detecting and Recognizing Human-Object Interactions
Autor:
Gkioxari, Georgia
;
Girshick, Ross
;
Dollar, Piotr
;
He, Kaiming
Assuntos:
Feature extraction
;
Image recognition
;
Object detection
;
Predictive models
;
Target recognition
;
Task analysis
;
Visualization
É parte de:
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, p.8359-8367
Descrição:
To understand the visual world, a machine must not only recognize individual object instances but also how they interact.
Humans
are often at the center of such interactions and detecting human-object interactions is an important practical and scientific problem. In this paper, we address the task of detecting (human, verb, object) triplets in challenging everyday photos. We propose a novel model that is driven by a human-centric approach. Our hypothesis is that the appearance of a person - their pose, clothing, action - is a powerful cue for localizing the objects they are interacting with. To exploit this cue, our model learns to predict an action-specific density over target object locations based on the appearance of a detected person. Our model also jointly learns to detect people and objects, and by fusing these predictions it efficiently infers interaction triplets in a clean, jointly trained end-to-end system we call InteractNet. We validate our approach on the recently introduced Verbs in COCO (V-COCO) and HICO-DET datasets, where we show quantitatively compelling results.
Editor:
IEEE
Idioma:
Inglês