Given a short query with keyphrases about a research idea in 2018 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: A Multimodal Factorization Model (MFM) that factorizes multimodal representations into

Keyphrases: multimodal representation learning text, image, video

Dataset description:
MobiBits: A novel database comprising representations of five different biometric characteristics, collected in a mobile, unconstrained or semi-constrained setting with three different mobile devices, including characteristics previously unavailable in existing datasets, namely hand images, thermal hand images, and thermal face images, all acquired with a mobile, off-the-shelf device.
IEMOCAP: Multimodal Emotion Recognition IEMOCAP The IEMOCAP dataset consists of 151 videos of recorded dialogues, with 2 speakers per session for a total of 302 videos across the dataset. Each segment is annotated for the presence of 9 emotions (angry, excited, fear, sad, surprised, frustrated, happy, disappointed and neutral) as well as valence, arousal and dominance.
dSprites: dSprites is a dataset of 2D shapes procedurally generated from 6 ground truth independent latent factors. These factors are color, shape, scale, rotation, x and y positions of a sprite.
Hindi Visual Genome: Hindi Visual Genome is a multimodal dataset consisting of text and images suitable for English-Hindi multimodal machine translation task and multimodal research.

Options:- A: MobiBits  B: IEMOCAP  C: dSprites  D: Hindi Visual Genome
IEMOCAP