Video Object Linguistic Grounding

•

1 j'aime•350 vues

The goal of this work is segmenting on a video sequence the objects which are mentioned in a linguistic description of the scene. We have adapted an existing deep neural network that achieves state of the art performance in semi-supervised video object segmentation, to add a linguistic branch that would generate an attention map over the video frames, making the segmentation of the objects temporally consistent along the sequence.

Données & analyses

Video Object
Linguistic GroundingWorkshop on
Multimodal
Understanding &
Learning for
Embodied
Applications
(MULEA)
Nice, France
25 October 2019
Carles
Ventura
Alba M.
Herrera
Xavier
Giro-i-Nieto

#RVOS Carles Ventura, Miriam Bellver, Andreu Girbau, Amaia Salvador, Ferran Marques and Xavier Giro-i-Nieto. “RVOS:
End-to-End Recurrent Network for Video Object Segmentation”, CVPR 2019.

4
From Masks to Referring Expressions
Model
time
Model
One-shot RVOS [*]
Model
time
Model
Referring expression
“the woman”
[*] #RVOS Carles Ventura, Miriam Bellver, Andreu Girbau, Amaia Salvador, Ferran Marques and Xavier Giro-i-Nieto. “RVOS:
End-to-End Recurrent Network for Video Object Segmentation”, CVPR 2019.

5
Related Work
Khoreva, A., Rohrbach, A., & Schiele, B. Video object segmentation with language referring expressions. ACCV 2018.

6
Image Segmentation with Refers
#MAttNet Yu, L., Lin, Z., Shen, X., Yang, J., Lu, X., Bansal, M., & Berg, T. L. . Mattnet: Modular attention network for referring
expression comprehension. CVPR 2018

8
Qualitative Results
FRAME 0 FRAME 2 FRAME 4
REFERRING EXPRESSIONS:
"a brown deer on the left"
"a brown deer on the right with branched horns"

Merci beaucoup
xavier.giro@upc.edu
@DocXavi
Xavier
Giro-i-Nieto

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