Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MultiBench: Multiscale Benchmarks for Multimodal Representation Learning   Learning multimodal representations involves integrating information from
multiple heterogeneous sources of data. It is a challenging yet crucial area
with numerous real-world applications in multimedia, affective computing,
robotics, finance, human-computer interaction, and healthcare. Unfortunately,
multimodal research has seen limited resources to study (1) generalization
across domains and modalities, (2) complexity during training and inference,
and (3) robustness to noisy and missing modalities. In order to accelerate
progress towards understudied modalities and tasks while ensuring real-world
robustness, we release MultiBench, a systematic and unified large-scale
benchmark spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6
research areas. MultiBench provides an automated end-to-end machine learning
pipeline that simplifies and standardizes data loading, experimental setup, and
model evaluation. To enable holistic evaluation, MultiBench offers a
comprehensive methodology to assess (1) generalization, (2) time and space
complexity, and (3) modality robustness. MultiBench introduces impactful
challenges for future research, including scalability to large-scale multimodal
datasets and robustness to realistic imperfections. To accompany this
benchmark, we also provide a standardized implementation of 20 core approaches
in multimodal learning. Simply applying methods proposed in different research
areas can improve the state-of-the-art performance on 9/15 datasets. Therefore,
MultiBench presents a milestone in unifying disjoint efforts in multimodal
research and paves the way towards a better understanding of the capabilities
and limitations of multimodal models, all the while ensuring ease of use,
accessibility, and reproducibility. MultiBench, our standardized code, and
leaderboards are publicly available, will be regularly updated, and welcomes
inputs from the community.

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The categories of this paper are: gr-qc
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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: eess
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The categories of this paper are: physics
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
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The categories of this paper are: econ