TY - GEN
T1 - Object Localization with Attribute Preference Based on Top-Down Attention
AU - Banik, Soubarna
AU - Lauri, Mikko
AU - Knoll, Alois
AU - Frintrop, Simone
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - We propose a weakly-supervised approach for object localization based on top-down attention which is able to consider both attributes and object classes as attentional cues. This enables to not only search for objects but additionally for objects with specific attributes such as colors or shapes. Our approach consists of two streams: an attribute stream and an object stream. By tracing backward through these two streams and localizing activated neurons in hidden layers, we generate two top-down attention maps, one for attributes and one for objects. Fusing these maps generates a joint attention map, which highlights regions with a specific attribute and object. We show experimentally that our method can localize objects in cluttered images by only specifying their attributes, and that instances of the same class can be discriminated based on their attributes.
AB - We propose a weakly-supervised approach for object localization based on top-down attention which is able to consider both attributes and object classes as attentional cues. This enables to not only search for objects but additionally for objects with specific attributes such as colors or shapes. Our approach consists of two streams: an attribute stream and an object stream. By tracing backward through these two streams and localizing activated neurons in hidden layers, we generate two top-down attention maps, one for attributes and one for objects. Fusing these maps generates a joint attention map, which highlights regions with a specific attribute and object. We show experimentally that our method can localize objects in cluttered images by only specifying their attributes, and that instances of the same class can be discriminated based on their attributes.
KW - Object attribute
KW - Object localization
KW - Top-down attention
UR - https://www.scopus.com/pages/publications/85115863567
U2 - 10.1007/978-3-030-87156-7_3
DO - 10.1007/978-3-030-87156-7_3
M3 - Conference contribution
AN - SCOPUS:85115863567
SN - 9783030871550
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 28
EP - 40
BT - Computer Vision Systems - 13th International Conference, ICVS 2021, Proceedings
A2 - Vincze, Markus
A2 - Patten, Timothy
A2 - Christensen, Henrik I
A2 - Nalpantidis, Lazaros
A2 - Liu, Ming
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Computer Vision Systems, ICVS 2021
Y2 - 22 September 2021 through 24 September 2021
ER -