TY - GEN
T1 - Attention-Based Multi-modal Emotion Recognition from Art
AU - Tashu, Tsegaye Misikir
AU - Horváth, Tomáš
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Emotions are very important in dealing with human decisions, interactions, and cognitive processes. Art is an imaginative human creation that should be appreciated, thought-provoking, and elicits an emotional response. The automatic recognition of emotions triggered by art is of considerable importance. It can be used to categorize artworks according to the emotions they evoke, recommend paintings that accentuate or balance a particular mood, and search for paintings of a particular style or genre that represent custom content in a custom state of impact. In this paper, we propose an attention-based multi-modal approach to emotion recognition that aims to use information from both the painting and title channels to achieve more accurate emotion recognition. Experimental results on the WikiArt emotion dataset showed the efficiency of the model we proposed and the usefulness of image and text modalities in emotion recognition.
AB - Emotions are very important in dealing with human decisions, interactions, and cognitive processes. Art is an imaginative human creation that should be appreciated, thought-provoking, and elicits an emotional response. The automatic recognition of emotions triggered by art is of considerable importance. It can be used to categorize artworks according to the emotions they evoke, recommend paintings that accentuate or balance a particular mood, and search for paintings of a particular style or genre that represent custom content in a custom state of impact. In this paper, we propose an attention-based multi-modal approach to emotion recognition that aims to use information from both the painting and title channels to achieve more accurate emotion recognition. Experimental results on the WikiArt emotion dataset showed the efficiency of the model we proposed and the usefulness of image and text modalities in emotion recognition.
KW - Emotion analysis
KW - Emotion recognition
KW - Multi-modal
UR - http://www.scopus.com/inward/record.url?scp=85104291476&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-68796-0_43
DO - 10.1007/978-3-030-68796-0_43
M3 - Conference contribution
AN - SCOPUS:85104291476
SN - 9783030687953
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 604
EP - 612
BT - Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings
A2 - Del Bimbo, Alberto
A2 - Cucchiara, Rita
A2 - Sclaroff, Stan
A2 - Farinella, Giovanni Maria
A2 - Mei, Tao
A2 - Bertini, Marco
A2 - Escalante, Hugo Jair
A2 - Vezzani, Roberto
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Pattern Recognition Workshops, ICPR 2020
Y2 - 10 January 2021 through 11 January 2021
ER -