Modality-Balanced Learning for Multimedia Recommendation

• URL: http://arxiv.org/abs/2408.06360v1
• Date: Fri, 26 Jul 2024 07:53:01 GMT
• Title: Modality-Balanced Learning for Multimedia Recommendation
• Authors: Jinghao Zhang, Guofan Liu, Qiang Liu, Shu Wu, Liang Wang,
• Abstract summary: We propose a Counterfactual Knowledge Distillation method to solve the imbalance problem and make the best use of all modalities. We also design a novel generic-and-specific distillation loss to guide the multimodal student to learn wider-and-deeper knowledge from teachers. Our method could serve as a plug-and-play module for both late-fusion and early-fusion backbones.
• Score: 21.772064939915214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many recommender models have been proposed to investigate how to incorporate multimodal content information into traditional collaborative filtering framework effectively. The use of multimodal information is expected to provide more comprehensive information and lead to superior performance. However, the integration of multiple modalities often encounters the modal imbalance problem: since the information in different modalities is unbalanced, optimizing the same objective across all modalities leads to the under-optimization problem of the weak modalities with a slower convergence rate or lower performance. Even worse, we find that in multimodal recommendation models, all modalities suffer from the problem of insufficient optimization. To address these issues, we propose a Counterfactual Knowledge Distillation method that could solve the imbalance problem and make the best use of all modalities. Through modality-specific knowledge distillation, it could guide the multimodal model to learn modality-specific knowledge from uni-modal teachers. We also design a novel generic-and-specific distillation loss to guide the multimodal student to learn wider-and-deeper knowledge from teachers. Additionally, to adaptively recalibrate the focus of the multimodal model towards weaker modalities during training, we estimate the causal effect of each modality on the training objective using counterfactual inference techniques, through which we could determine the weak modalities, quantify the imbalance degree and re-weight the distillation loss accordingly. Our method could serve as a plug-and-play module for both late-fusion and early-fusion backbones. Extensive experiments on six backbones show that our proposed method can improve the performance by a large margin. The source code will be released at \url{https://github.com/CRIPAC-DIG/Balanced-Multimodal-Rec}

Related papers

• On-the-fly Modulation for Balanced Multimodal Learning [53.616094855778954]
  Multimodal learning is expected to boost model performance by integrating information from different modalities. The widely-used joint training strategy leads to imbalanced and under-optimized uni-modal representations. We propose On-the-fly Prediction Modulation (OPM) and On-the-fly Gradient Modulation (OGM) strategies to modulate the optimization of each modality.
  arXiv  Detail & Related papers  (2024-10-15T13:15:50Z)
• 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)
• Diagnosing and Re-learning for Balanced Multimodal Learning [8.779005254634857]
  We propose the Diagnosing & Re-learning method to overcome the imbalanced multimodal learning problem. The learning state of each modality is estimated based on the separability of its uni-modal representation space. In this way, the over-emphasizing of scarcely informative modalities is avoided.
  arXiv  Detail & Related papers  (2024-07-12T22:12:03Z)
• MMPareto: Boosting Multimodal Learning with Innocent Unimodal Assistance [10.580712937465032]
  We identify the previously ignored gradient conflict between multimodal and unimodal learning objectives. We propose MMPareto algorithm, which could ensure a final gradient with direction common to all learning objectives. Our method is also expected to facilitate multi-task cases with a clear discrepancy in task difficulty.
  arXiv  Detail & Related papers  (2024-05-28T01:19:13Z)
• 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)
• MinT: Boosting Generalization in Mathematical Reasoning via Multi-View Fine-Tuning [53.90744622542961]
  Reasoning in mathematical domains remains a significant challenge for small language models (LMs) We introduce a new method that exploits existing mathematical problem datasets with diverse annotation styles. Experimental results show that our strategy enables a LLaMA-7B model to outperform prior approaches.
  arXiv  Detail & Related papers  (2023-07-16T05:41:53Z)
• 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)
• Provable Dynamic Fusion for Low-Quality Multimodal Data [94.39538027450948]
  Dynamic multimodal fusion emerges as a promising learning paradigm. Despite its widespread use, theoretical justifications in this field are still notably lacking. This paper provides theoretical understandings to answer this question under a most popular multimodal fusion framework from the generalization perspective. A novel multimodal fusion framework termed Quality-aware Multimodal Fusion (QMF) is proposed, which can improve the performance in terms of classification accuracy and model robustness.
  arXiv  Detail & Related papers  (2023-06-03T08:32:35Z)
• Balanced Multimodal Learning via On-the-fly Gradient Modulation [10.5602074277814]
  Multimodal learning helps to comprehensively understand the world, by integrating different senses. We propose on-the-fly gradient modulation to adaptively control the optimization of each modality, via monitoring the discrepancy of their contribution towards the learning objective.
  arXiv  Detail & Related papers  (2022-03-29T08:26:38Z)

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.