Mind the Gap: A Framework for Assessing Pitfalls in Multimodal Active Learning
- URL: http://arxiv.org/abs/2603.29677v1
- Date: Tue, 31 Mar 2026 12:33:45 GMT
- Title: Mind the Gap: A Framework for Assessing Pitfalls in Multimodal Active Learning
- Abstract summary: Multimodal learning enables neural networks to integrate information from heterogeneous sources, but active learning in this setting faces distinct challenges.<n>These include missing modalities, differences in modality difficulty, and varying interaction structures.<n>We introduce a new framework for benchmarking multimodal active learning that isolates these pitfalls using synthetic datasets.
- Score: 10.704544380420456
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Multimodal learning enables neural networks to integrate information from heterogeneous sources, but active learning in this setting faces distinct challenges. These include missing modalities, differences in modality difficulty, and varying interaction structures. These are issues absent in the unimodal case. While the behavior of active learning strategies in unimodal settings is well characterized, their behavior under such multimodal conditions remains poorly understood. We introduce a new framework for benchmarking multimodal active learning that isolates these pitfalls using synthetic datasets, allowing systematic evaluation without confounding noise. Using this framework, we compare unimodal and multimodal query strategies and validate our findings on two real-world datasets. Our results show that models consistently develop imbalanced representations, relying primarily on one modality while neglecting others. Existing query methods do not mitigate this effect, and multimodal strategies do not consistently outperform unimodal ones. These findings highlight limitations of current active learning methods and underline the need for modality-aware query strategies that explicitly address these pitfalls. Code and benchmark resources will be made publicly available.
Related papers
- Co-Learning for Missing Arbitrary Modalities in Multi-modal Classification [5.850980612003824]
We introduce two approaches that leverage information at both feature- and decision-level to handle missing modalities.<n>Experiments on two multi-modal classification benchmarks demonstrate significant gains in various missing modality conditions.
arXiv Detail & Related papers (2026-07-27T17:23:30Z) - From Sparse Decisions to Dense Reasoning: A Multi-attribute Trajectory Paradigm for Multimodal Moderation [59.27094165576015]
We propose a novel learning paradigm (UniMod) that transitions from sparse decision-making to dense reasoning traces.<n>By constructing structured trajectories encompassing evidence grounding, modality assessment, risk mapping, policy decision, and response generation, we reformulate monolithic decision tasks into a multi-dimensional boundary learning process.<n>We introduce specialized optimization strategies to decouple task-specific parameters and rebalance training dynamics, effectively resolving interference between diverse objectives in multi-task learning.
arXiv Detail & Related papers (2026-01-28T09:29:40Z) - Multimodal Negative Learning [55.67017420486548]
We propose a new learning paradigm: "Learning Not to be" (Negative Learning)<n>Instead of enhancing weak modalities' target-class predictions, the dominant modalities dynamically guide the weak modality to suppress non-target classes.<n>This stabilizes the decision space and preserves modality-specific information.
arXiv Detail & Related papers (2025-10-23T11:47:11Z) - 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) - 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) - Towards Balanced Active Learning for Multimodal Classification [15.338417969382212]
Training multimodal networks requires a vast amount of data due to their larger parameter space compared to unimodal networks.
Active learning is a widely used technique for reducing data annotation costs by selecting only those samples that could contribute to improving model performance.
Current active learning strategies are mostly designed for unimodal tasks, and when applied to multimodal data, they often result in biased sample selection from the dominant modality.
arXiv Detail & Related papers (2023-06-14T07:23:36Z) - On Modality Bias Recognition and Reduction [70.69194431713825]
We study the modality bias problem in the context of multi-modal classification.
We propose a plug-and-play loss function method, whereby the feature space for each label is adaptively learned.
Our method yields remarkable performance improvements compared with the baselines.
arXiv Detail & Related papers (2022-02-25T13:47:09Z)
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.