Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning

• URL: http://arxiv.org/abs/2505.19614v1
• Date: Mon, 26 May 2025 07:30:38 GMT
• Title: Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning
• Authors: Sanghyuk Chun,
• Abstract summary: This position paper argues that multiplicity is a fundamental bottleneck that manifests across all stages of the multimodal learning pipeline.<n>It highlights how multiplicity introduces training uncertainty, unreliable evaluation, and low dataset quality.
• Score: 20.00929281001257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal learning has seen remarkable progress, particularly with the emergence of large-scale pre-training across various modalities. However, most current approaches are built on the assumption of a deterministic, one-to-one alignment between modalities. This oversimplifies real-world multimodal relationships, where their nature is inherently many-to-many. This phenomenon, named multiplicity, is not a side-effect of noise or annotation error, but an inevitable outcome of semantic abstraction, representational asymmetry, and task-dependent ambiguity in multimodal tasks. This position paper argues that multiplicity is a fundamental bottleneck that manifests across all stages of the multimodal learning pipeline: from data construction to training and evaluation. This paper examines the causes and consequences of multiplicity, and highlights how multiplicity introduces training uncertainty, unreliable evaluation, and low dataset quality. This position calls for new research directions on multimodal learning: novel multiplicity-aware learning frameworks and dataset construction protocols considering multiplicity.

Related papers

• Continual Multimodal Contrastive Learning [70.60542106731813]
  Multimodal contrastive learning (MCL) advances in aligning different modalities and generating multimodal representations in a joint space.<n>However, a critical yet often overlooked challenge remains: multimodal data is rarely collected in a single process, and training from scratch is computationally expensive.<n>In this paper, we formulate CMCL through two specialized principles of stability and plasticity.<n>We theoretically derive a novel optimization-based method, which projects updated gradients from dual sides onto subspaces where any gradient is prevented from interfering with the previously learned knowledge.
  arXiv  Detail & Related papers  (2025-03-19T07:57:08Z)
• Missing Modality Prediction for Unpaired Multimodal Learning via Joint Embedding of Unimodal Models [6.610033827647869]
  In real-world scenarios, consistently acquiring complete multimodal data presents significant challenges. This often leads to the issue of missing modalities, where data for certain modalities are absent. We propose a novel framework integrating parameter-efficient fine-tuning of unimodal pretrained models with a self-supervised joint-embedding learning method.
  arXiv  Detail & Related papers  (2024-07-17T14:44:25Z)
• Beyond Unimodal Learning: The Importance of Integrating Multiple Modalities for Lifelong Learning [23.035725779568587]
  We study the role and interactions of multiple modalities in mitigating forgetting in deep neural networks (DNNs) Our findings demonstrate that leveraging multiple views and complementary information from multiple modalities enables the model to learn more accurate and robust representations. We propose a method for integrating and aligning the information from different modalities by utilizing the relational structural similarities between the data points in each modality.
  arXiv  Detail & Related papers  (2024-05-04T22:02:58Z)
• Multimodal Representation Learning by Alternating Unimodal Adaptation [73.15829571740866]
  We propose MLA (Multimodal Learning with Alternating Unimodal Adaptation) to overcome challenges where some modalities appear more dominant than others during multimodal learning. MLA reframes the conventional joint multimodal learning process by transforming it into an alternating unimodal learning process. It captures cross-modal interactions through a shared head, which undergoes continuous optimization across different modalities. Experiments are conducted on five diverse datasets, encompassing scenarios with complete modalities and scenarios with missing modalities.
  arXiv  Detail & Related papers  (2023-11-17T18:57:40Z)
• Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding [62.70450216120704]
  Unsupervised pre-training has shown great success in skeleton-based action understanding. We propose a Unified Multimodal Unsupervised Representation Learning framework, called UmURL. UmURL exploits an efficient early-fusion strategy to jointly encode the multi-modal features in a single-stream manner.
  arXiv  Detail & Related papers  (2023-11-06T13:56:57Z)
• Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation [16.17270247327955]
  We propose a simple and parameter-efficient adaptation procedure for pretrained multimodal networks. We demonstrate that such adaptation can partially bridge performance drop due to missing modalities. Our proposed method demonstrates versatility across various tasks and datasets, and outperforms existing methods for robust multimodal learning with missing modalities.
  arXiv  Detail & Related papers  (2023-10-06T03:04:21Z)
• Decoupling Common and Unique Representations for Multimodal Self-supervised Learning [22.12729786091061]
  We propose Decoupling Common and Unique Representations (DeCUR), a simple yet effective method for multimodal self-supervised learning. By distinguishing inter- and intra-modal embeddings through multimodal redundancy reduction, DeCUR can integrate complementary information across different modalities.
  arXiv  Detail & Related papers  (2023-09-11T08:35:23Z)
• Learning Unseen Modality Interaction [54.23533023883659]
  Multimodal learning assumes all modality combinations of interest are available during training to learn cross-modal correspondences. We pose the problem of unseen modality interaction and introduce a first solution. It exploits a module that projects the multidimensional features of different modalities into a common space with rich information preserved.
  arXiv  Detail & Related papers  (2023-06-22T10:53:10Z)
• Identifiability Results for Multimodal Contrastive Learning [72.15237484019174]
  We show that it is possible to recover shared factors in a more general setup than the multi-view setting studied previously. Our work provides a theoretical basis for multimodal representation learning and explains in which settings multimodal contrastive learning can be effective in practice.
  arXiv  Detail & Related papers  (2023-03-16T09:14:26Z)
• Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis [19.07020276666615]
  We propose a novel framework named MultiModal Contrastive Learning (MMCL) for multimodal representation to capture intra- and inter-modality dynamics simultaneously. We also design two contrastive learning tasks, instance- and sentiment-based contrastive learning, to promote the process of prediction and learn more interactive information related to sentiment.
  arXiv  Detail & Related papers  (2022-10-26T08:24:15Z)
• High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning [112.51498431119616]
  This paper studies efficient representation learning for high-modality scenarios involving a large set of diverse modalities. A single model, HighMMT, scales up to 10 modalities (text, image, audio, video, sensors, proprioception, speech, time-series, sets, and tables) and 15 tasks from 5 research areas.
  arXiv  Detail & Related papers  (2022-03-02T18:56:20Z)
• Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction [125.18248926508045]
  We propose Channel-Exchanging-Network (CEN) which is self-adaptive, parameter-free, and more importantly, applicable for both multimodal fusion and multitask learning. CEN dynamically exchanges channels betweenworks of different modalities. For the application of dense image prediction, the validity of CEN is tested by four different scenarios.
  arXiv  Detail & Related papers  (2021-12-04T05:47:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.