An Attention-based Model for Robust Forecasting with Missing Modality

• URL: http://arxiv.org/abs/2606.13970v1
• Date: Thu, 11 Jun 2026 23:24:38 GMT
• Title: An Attention-based Model for Robust Forecasting with Missing Modality
• Abstract summary: We introduce a multimodal model designed to handle missing modalities during both training and inference.<n>We show that our proposed model can be trained with missing modalities while approximating a robust representation of all modalities.
• Score: 5.076667782726574
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Learning with missing modalities is a fundamental challenge in multimodal robot learning, as real-world robotic systems often operate in environments with incomplete sensor data. Attention-based models are appealing for processing multimodal data because they can handle multiple modalities with a single backbone network. However, most multimodal models assume that all modalities are available during both training and inference, limiting their applicability in robotic perception and decision-making. In this paper, we introduce a multimodal model designed to handle missing modalities during both training and inference. The model is formulated as a conditional variational autoencoder (CVAE) and incorporates a transformer-based architecture that leverages attention mechanisms to learn a unified, fixed-dimensional representation, even when some modalities are missing. We show that our proposed model can be trained with missing modalities while approximating a robust representation of all modalities. We evaluate our approach on five multimodal datasets across two robot learning tasks: human trajectory prediction and robot manipulation forecasting. Experimental results demonstrate that our model effectively learns from incomplete data and is superior to prior multimodal fusion approaches.

Related papers

• 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)
• Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization [14.606035444283984]
  Current approaches focus on developing models that handle modality-incomplete inputs during inference. We propose a robust universal model with modality reconstruction and model personalization. Our method has been extensively validated on two brain tumor segmentation benchmarks.
  arXiv  Detail & Related papers  (2024-06-04T06:07:24Z)
• An Interactive Agent Foundation Model [49.77861810045509]
  We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents. Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction. We demonstrate the performance of our framework across three separate domains -- Robotics, Gaming AI, and Healthcare.
  arXiv  Detail & Related papers  (2024-02-08T18:58:02Z)
• MultiDelete for Multimodal Machine Unlearning [14.755831733659699]
  MultiDelete is designed to decouple associations between unimodal data points during unlearning. It can maintain the multimodal and unimodal knowledge of the original model post unlearning. It can provide better protection to unlearned data against adversarial attacks.
  arXiv  Detail & Related papers  (2023-11-18T08:30:38Z)
• What Makes for Robust Multi-Modal Models in the Face of Missing Modalities? [35.19295402483624]
  We model the scenarios of multi-modal models encountering missing modalities from an information-theoretic perspective. We introduce Uni-Modal Ensemble with Missing Modality Adaptation (UME-MMA) UME-MMA employs uni-modal pre-trained weights for the multi-modal model to enhance feature extraction and utilizes missing modality data augmentation techniques to better adapt to situations with missing modalities.
  arXiv  Detail & Related papers  (2023-10-10T07:47:57Z)
• 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)
• Transferring Foundation Models for Generalizable Robotic Manipulation [82.12754319808197]
  We propose a novel paradigm that effectively leverages language-reasoning segmentation mask generated by internet-scale foundation models.<n>Our approach can effectively and robustly perceive object pose and enable sample-efficient generalization learning.<n>Demos can be found in our submitted video, and more comprehensive ones can be found in link1 or link2.
  arXiv  Detail & Related papers  (2023-06-09T07:22:12Z)
• MultiViz: An Analysis Benchmark for Visualizing and Understanding Multimodal Models [103.9987158554515]
  MultiViz is a method for analyzing the behavior of multimodal models by scaffolding the problem of interpretability into 4 stages. We show that the complementary stages in MultiViz together enable users to simulate model predictions, assign interpretable concepts to features, perform error analysis on model misclassifications, and use insights from error analysis to debug models.
  arXiv  Detail & Related papers  (2022-06-30T18:42:06Z)
• Learning Sequential Latent Variable Models from Multimodal Time Series Data [6.107812768939553]
  We present a self-supervised generative modelling framework to jointly learn a probabilistic latent state representation of multimodal data. We demonstrate that our approach leads to significant improvements in prediction and representation quality.
  arXiv  Detail & Related papers  (2022-04-21T21:59:24Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• Model-Based Visual Planning with Self-Supervised Functional Distances [104.83979811803466]
  We present a self-supervised method for model-based visual goal reaching. Our approach learns entirely using offline, unlabeled data. We find that this approach substantially outperforms both model-free and model-based prior methods.
  arXiv  Detail & Related papers  (2020-12-30T23:59:09Z)

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.