TY - GEN

T1 - MUWS 2024

T2 - 2024 International Conference on Multimedia Retrieval, ICMR 2024

AU - Kastner, Marc A.

AU - Cheema, Gullal S.

AU - Hakimov, Sherzod

AU - Garcia, Noa

N1 - Publisher Copyright: © 2024 Copyright held by the owner/author(s).

PY - 2024/6/7

Y1 - 2024/6/7

N2 - Multimodal human understanding and analysis are emerging research areas that cut through several disciplines like Computer Vision (CV), Natural Language Processing (NLP), Speech Processing, Human-Computer Interaction (HCI), and Multimedia. Several multimodal learning techniques have recently shown the benefit of combining multiple modalities in image-text, audio-visual and video representation learning and various downstream multimodal tasks. At the core, these methods focus on modelling the modalities and their complex interactions by using large amounts of data, different loss functions and deep neural network architectures. However, for many Web and Social media applications, there is the need to model the human, including the understanding of human behaviour and perception. For this, it becomes important to consider interdisciplinary approaches, including social sciences and psychology. The core is understanding various cross-modal relations, quantifying bias such as social biases, and the applicability of models to real-world problems. Interdisciplinary theories such as semiotics or gestalt psychology can provide additional insights on perceptual understanding through signs and symbols across multiple modalities. In general, these theories provide a compelling view of multimodality and perception that can further expand computational research and multimedia applications on the Web and Social media. The theme of the MUWS workshop, multimodal human understanding, includes various interdisciplinary challenges related to social bias analyses, multimodal representation learning, detection of human impressions or sentiment, hate speech, sarcasm in multimodal data, multimodal rhetoric and semantics, and related topics. The MUWS workshop is an interactive event and includes keynotes by relevant experts, a poster session, research presentations and discussion.

AB - Multimodal human understanding and analysis are emerging research areas that cut through several disciplines like Computer Vision (CV), Natural Language Processing (NLP), Speech Processing, Human-Computer Interaction (HCI), and Multimedia. Several multimodal learning techniques have recently shown the benefit of combining multiple modalities in image-text, audio-visual and video representation learning and various downstream multimodal tasks. At the core, these methods focus on modelling the modalities and their complex interactions by using large amounts of data, different loss functions and deep neural network architectures. However, for many Web and Social media applications, there is the need to model the human, including the understanding of human behaviour and perception. For this, it becomes important to consider interdisciplinary approaches, including social sciences and psychology. The core is understanding various cross-modal relations, quantifying bias such as social biases, and the applicability of models to real-world problems. Interdisciplinary theories such as semiotics or gestalt psychology can provide additional insights on perceptual understanding through signs and symbols across multiple modalities. In general, these theories provide a compelling view of multimodality and perception that can further expand computational research and multimedia applications on the Web and Social media. The theme of the MUWS workshop, multimodal human understanding, includes various interdisciplinary challenges related to social bias analyses, multimodal representation learning, detection of human impressions or sentiment, hate speech, sarcasm in multimodal data, multimodal rhetoric and semantics, and related topics. The MUWS workshop is an interactive event and includes keynotes by relevant experts, a poster session, research presentations and discussion.

KW - human understanding

KW - image-text relations

KW - machine learning

KW - multimodality

KW - semiotics

KW - social media

KW - web

UR - http://www.scopus.com/inward/record.url?scp=85199205906&partnerID=8YFLogxK

U2 - 10.1145/3652583.3658893

DO - 10.1145/3652583.3658893

M3 - Conference contribution

AN - SCOPUS:85199205906

SP - 1342

EP - 1344

BT - ICMR '24

Y2 - 10 June 2024 through 14 June 2024

ER -