Can multimodal representation learning by alignment preserve modality-specific information?

• URL: http://arxiv.org/abs/2509.17943v1
• Date: Mon, 22 Sep 2025 16:06:10 GMT
• Title: Can multimodal representation learning by alignment preserve modality-specific information?
• Authors: Romain Thoreau, Jessie Levillain, Dawa Derksen,
• Abstract summary: multimodal representation learning techniques leverage the spatial alignment between satellite data from different modalities acquired over the same geographic area.<n>We show, under simplifying assumptions, when alignment strategies fundamentally lead to an information loss.<n>We hope to support new developments in contrastive learning for the combination of multimodal satellite data.
• Score: 2.0816054646359805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Combining multimodal data is a key issue in a wide range of machine learning tasks, including many remote sensing problems. In Earth observation, early multimodal data fusion methods were based on specific neural network architectures and supervised learning. Ever since, the scarcity of labeled data has motivated self-supervised learning techniques. State-of-the-art multimodal representation learning techniques leverage the spatial alignment between satellite data from different modalities acquired over the same geographic area in order to foster a semantic alignment in the latent space. In this paper, we investigate how this methods can preserve task-relevant information that is not shared across modalities. First, we show, under simplifying assumptions, when alignment strategies fundamentally lead to an information loss. Then, we support our theoretical insight through numerical experiments in more realistic settings. With those theoretical and empirical evidences, we hope to support new developments in contrastive learning for the combination of multimodal satellite data. Our code and data is publicly available at https://github.com/Romain3Ch216/alg_maclean_25.

Related papers

• Spatial Knowledge Graph-Guided Multimodal Synthesis [78.11669780958657]
  We introduce a novel multimodal synthesis approach guided by spatial knowledge graphs, grounded in the concept of knowledge-to-data generation.<n>In experiments, data synthesized from diverse types of spatial knowledge, including direction and distance, enhance the spatial perception and reasoning abilities of MLLMs markedly.<n>We hope that the idea of knowledge-based data synthesis can advance the development of spatial intelligence.
  arXiv  Detail & Related papers  (2025-05-28T17:50:21Z)
• 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)
• An Information Criterion for Controlled Disentanglement of Multimodal Data [39.601584166020274]
  Multimodal representation learning seeks to relate and decompose information inherent in multiple modalities.<n>Disentangled Self-Supervised Learning (DisentangledSSL) is a novel self-supervised approach for learning disentangled representations.
  arXiv  Detail & Related papers  (2024-10-31T14:57:31Z)
• Self-Supervised Multimodal Learning: A Survey [23.526389924804207]
  Multimodal learning aims to understand and analyze information from multiple modalities. The heavy dependence on data paired with expensive human annotations impedes scaling up models. Given the availability of large-scale unannotated data in the wild, self-supervised learning has become an attractive strategy to alleviate the annotation bottleneck.
  arXiv  Detail & Related papers  (2023-03-31T16:11:56Z)
• Vision+X: A Survey on Multimodal Learning in the Light of Data [64.03266872103835]
  multimodal machine learning that incorporates data from various sources has become an increasingly popular research area. We analyze the commonness and uniqueness of each data format mainly ranging from vision, audio, text, and motions. We investigate the existing literature on multimodal learning from both the representation learning and downstream application levels.
  arXiv  Detail & Related papers  (2022-10-05T13:14:57Z)
• Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning [77.34726150561087]
  We propose an approach for creating a multi-modal and large-temporal dataset comprised of publicly available Remote Sensing data. We use Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation.
  arXiv  Detail & Related papers  (2022-09-28T18:51:59Z)
• 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)
• Generalized Zero-Shot Learning using Multimodal Variational Auto-Encoder with Semantic Concepts [0.9054540533394924]
  Recent techniques try to learn a cross-modal mapping between the semantic space and the image space. We propose a Multimodal Variational Auto-Encoder (M-VAE) which can learn the shared latent space of image features and the semantic space. Our results show that our proposed model outperforms the current state-of-the-art approaches for generalized zero-shot learning.
  arXiv  Detail & Related papers  (2021-06-26T20:08:37Z)
• Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction [10.646114896709717]
  In multimodal data analysis, not all observations would show the same level of reliability or information quality. We propose an adaptive approach for dimensionality reduction to overcome this issue. We test our approach on multimodal datasets acquired in diverse research fields.
  arXiv  Detail & Related papers  (2021-05-08T11:53:12Z)
• Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos [69.61522804742427]
  This paper proposes a self-supervised training framework that learns a common multimodal embedding space. We extend the concept of instance-level contrastive learning with a multimodal clustering step to capture semantic similarities across modalities. The resulting embedding space enables retrieval of samples across all modalities, even from unseen datasets and different domains.
  arXiv  Detail & Related papers  (2021-04-26T15:55:01Z)
• Relation-Guided Representation Learning [53.60351496449232]
  We propose a new representation learning method that explicitly models and leverages sample relations. Our framework well preserves the relations between samples. By seeking to embed samples into subspace, we show that our method can address the large-scale and out-of-sample problem.
  arXiv  Detail & Related papers  (2020-07-11T10:57:45Z)
• Auxiliary-task learning for geographic data with autoregressive embeddings [1.4823143667165382]
  We propose SXL, a method for embedding information on the autoregressive nature of spatial data directly into the learning process. We utilize the local Moran's I, a popular measure of local spatial autocorrelation, to "nudge" the model to learn the direction and magnitude of local spatial effects. We highlight how our method consistently improves the training of neural networks in unsupervised and supervised learning tasks.
  arXiv  Detail & Related papers  (2020-06-18T12:16:08Z)

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.