Pith. sign in

REVIEW 1 cited by

Affective Computing Has Changed: The Foundation Model Disruption

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2409.08907 v1 pith:H47GN664 submitted 2024-09-13 cs.AI cs.CLcs.CY

Björn Schuller , Adria Mallol-Ragolta , Alejandro Peña Almansa , Iosif Tsangko , Mostafa M. Amin , Anastasia Semertzidou , Lukas Christ , Shahin Amiriparian This is my paper · ORCID
classification cs.AIcs.CLcs.CY
keywords affectivefoundationmodelscomputinghandproblemsrelatedresearch
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The dawn of Foundation Models has on the one hand revolutionised a wide range of research problems, and, on the other hand, democratised the access and use of AI-based tools by the general public. We even observe an incursion of these models into disciplines related to human psychology, such as the Affective Computing domain, suggesting their affective, emerging capabilities. In this work, we aim to raise awareness of the power of Foundation Models in the field of Affective Computing by synthetically generating and analysing multimodal affective data, focusing on vision, linguistics, and speech (acoustics). We also discuss some fundamental problems, such as ethical issues and regulatory aspects, related to the use of Foundation Models in this research area.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Large Language Models for Depression Recognition in Spoken Language Integrating Psychological Knowledge

    cs.HC 2025-05 reject novelty 3.0 of 10

    A multimodal LLM pipeline with Wav2Vec audio and WHO-based Q&A knowledge injection reports small improvements on DAIC-WOZ, but the fusion adds nothing over audio-only and the baseline is cherry-picked.

Pith tools