CyIN: Cyclic Informative Latent Space for Bridging Complete and Incomplete Multimodal Learning

• URL: http://arxiv.org/abs/2602.04920v1
• Date: Wed, 04 Feb 2026 07:05:15 GMT
• Title: CyIN: Cyclic Informative Latent Space for Bridging Complete and Incomplete Multimodal Learning
• Authors: Ronghao Lin, Qiaolin He, Sijie Mai, Ying Zeng, Aolin Xiong, Li Huang, Yap-Peng Tan, Haifeng Hu,
• Abstract summary: We present a novel Cyclic INformative Learning framework (CyIN) to bridge the gap between complete and incomplete multimodal learning.<n>To supplement the missing information caused by incomplete multimodal input, we propose cross-modal cyclic translation.<n>CyIN succeeds in jointly optimizing complete and incomplete multimodal learning in one unified model.
• Score: 35.562458985015944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal machine learning, mimicking the human brain's ability to integrate various modalities has seen rapid growth. Most previous multimodal models are trained on perfectly paired multimodal input to reach optimal performance. In real-world deployments, however, the presence of modality is highly variable and unpredictable, causing the pre-trained models in suffering significant performance drops and fail to remain robust with dynamic missing modalities circumstances. In this paper, we present a novel Cyclic INformative Learning framework (CyIN) to bridge the gap between complete and incomplete multimodal learning. Specifically, we firstly build an informative latent space by adopting token- and label-level Information Bottleneck (IB) cyclically among various modalities. Capturing task-related features with variational approximation, the informative bottleneck latents are purified for more efficient cross-modal interaction and multimodal fusion. Moreover, to supplement the missing information caused by incomplete multimodal input, we propose cross-modal cyclic translation by reconstruct the missing modalities with the remained ones through forward and reverse propagation process. With the help of the extracted and reconstructed informative latents, CyIN succeeds in jointly optimizing complete and incomplete multimodal learning in one unified model. Extensive experiments on 4 multimodal datasets demonstrate the superior performance of our method in both complete and diverse incomplete scenarios.

Related papers

• Multi-Modal Continual Learning via Cross-Modality Adapters and Representation Alignment with Knowledge Preservation [23.701550460744738]
  Continual learning is essential for adapting models to new tasks while retaining previously acquired knowledge.<n>We propose a pre-trained model-based framework for multi-modal continual learning.<n>Our approach consistently outperforms baselines in both class-incremental and domain-incremental learning.
  arXiv  Detail & Related papers  (2025-11-10T05:33:41Z)
• NExT-OMNI: Towards Any-to-Any Omnimodal Foundation Models with Discrete Flow Matching [64.10695425442164]
  We introduce NExT-OMNI, an open-source omnimodal foundation model that achieves unified modeling through discrete flow paradigms.<n>Trained on large-scale interleaved text, image, video, and audio data, NExT-OMNI delivers competitive performance on multimodal generation and understanding benchmarks.<n>To advance further research, we release training details, data protocols, and open-source both the code and model checkpoints.
  arXiv  Detail & Related papers  (2025-10-15T16:25:18Z)
• Continual Multimodal Contrastive Learning [99.53621521696051]
  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)
• MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks [50.98856172702256]
  We propose the Modality-INformed knowledge Distillation (MIND) framework, a multimodal model compression approach.<n>MIND transfers knowledge from ensembles of pre-trained deep neural networks of varying sizes into a smaller multimodal student.<n>We evaluate MIND on binary and multilabel clinical prediction tasks using time series data and chest X-ray images.
  arXiv  Detail & Related papers  (2025-02-03T08:50:00Z)
• 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)
• Dynamic Self-adaptive Multiscale Distillation from Pre-trained Multimodal Large Model for Efficient Cross-modal Representation Learning [12.00246872965739]
  We propose a novel dynamic self-adaptive multiscale distillation from pre-trained multimodal large model. Our strategy employs a multiscale perspective, enabling the extraction structural knowledge across from the pre-trained multimodal large model. Our methodology streamlines pre-trained multimodal large models using only their output features and original image-level information.
  arXiv  Detail & Related papers  (2024-04-16T18:22:49Z)
• 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)
• 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)

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.