Alt-MoE:A Scalable Framework for Bidirectional Multimodal Alignment and Efficient Knowledge Integration

• URL: http://arxiv.org/abs/2409.05929v3
• Date: Fri, 20 Dec 2024 06:37:16 GMT
• Title: Alt-MoE:A Scalable Framework for Bidirectional Multimodal Alignment and Efficient Knowledge Integration
• Authors: Hongyang Lei, Xiaolong Cheng, Dan Wang, Kun Fan, Qi Qin, Huazhen Huang, Yetao Wu, Qingqing Gu, Zhonglin Jiang, Yong Chen, Luo Ji,
• Abstract summary: Multimodal learning has advanced significantly by aligning different modalities within shared latent spaces. Direct alignment struggles to fully leverage rich intra-modal knowledge, often requiring extensive training data to achieve cross-modal representation. We introduce Alt-MoE, a scalable multimodal alignment framework that employs a mixture of experts (MoE) model as a multi-directional connector across modalities.
• Score: 6.928469290518152
• License:
• Abstract: Multimodal learning has advanced significantly by aligning different modalities within shared latent spaces, enabling tasks such as cross-modal understanding and generation. Current alignment strategies in multimodal learning primarily include direct alignment using pre-trained or unified encoders and single-directional alignment via modality-specific connectors. Direct alignment struggles to fully leverage rich intra-modal knowledge, often requiring extensive training data to achieve cross-modal representation. Meanwhile, single-directional alignment methods, despite leveraging pre-trained knowledge, restrict task adaptability and hinder the model's ability to capture bidirectional relationships, leading to incomplete knowledge fusion and underutilization of complementary modality-specific information. To address these limitations, we introduce Alt-MoE, a scalable multimodal alignment framework that employs a mixture of experts (MoE) model as a multi-directional connector across modalities. By utilizing a sequential alternating one-way alignment strategy, Alt-MoE iteratively refines the model to achieve bidirectional alignment. Alt-MoE operates in latent space, enabling efficient vector pre-storage and real-time retrieval via MoE, optimizing large-scale data processing. Extensive empirical studies demonstrate that Alt-MoE achieves competitive performance on cross-modal retrieval and visual question answering by integrating diverse modality-specific knowledge, generalizing to unseen data, and easily scaling to new tasks and modalities through dynamic adjustment of MoE capacity and expert activation.

Related papers

• SM3Det: A Unified Model for Multi-Modal Remote Sensing Object Detection [73.49799596304418]
  This paper introduces a new task called Multi-Modal datasets and Multi-Task Object Detection (M2Det) for remote sensing. It is designed to accurately detect horizontal or oriented objects from any sensor modality. This task poses challenges due to 1) the trade-offs involved in managing multi-modal modelling and 2) the complexities of multi-task optimization.
  arXiv  Detail & Related papers  (2024-12-30T02:47:51Z)
• Towards Modality Generalization: A Benchmark and Prospective Analysis [56.84045461854789]
  This paper introduces Modality Generalization (MG), which focuses on enabling models to generalize to unseen modalities. We propose a comprehensive benchmark featuring multi-modal algorithms and adapt existing methods that focus on generalization. Our work provides a foundation for advancing robust and adaptable multi-modal models, enabling them to handle unseen modalities in realistic scenarios.
  arXiv  Detail & Related papers  (2024-12-24T08:38:35Z)
• Learning Robust Anymodal Segmentor with Unimodal and Cross-modal Distillation [30.33381342502258]
  Key challenge is unimodal bias, where multimodal segmentors over rely on certain modalities, causing performance drops when others are missing. We develop the first framework for learning robust segmentor that can handle any combinations of visual modalities.
  arXiv  Detail & Related papers  (2024-11-26T06:15:27Z)
• LLMs Can Evolve Continually on Modality for X-Modal Reasoning [62.2874638875554]
  Existing methods rely heavily on modal-specific pretraining and joint-modal tuning, leading to significant computational burdens when expanding to new modalities. We propose PathWeave, a flexible and scalable framework with modal-Path sWitching and ExpAnsion abilities. PathWeave performs comparably to state-of-the-art MLLMs while concurrently reducing parameter training burdens by 98.73%.
  arXiv  Detail & Related papers  (2024-10-26T13:19:57Z)
• Anchors Aweigh! Sail for Optimal Unified Multi-Modal Representations [16.036997801745905]
  Multimodal learning plays a crucial role in enabling machine learning models to fuse and utilize diverse data sources. Recent binding methods, such as ImageBind, typically use a fixed anchor modality to align multimodal data in the anchor modal embedding space. We propose CentroBind, a simple yet powerful approach that eliminates the need for a fixed anchor.
  arXiv  Detail & Related papers  (2024-10-02T23:19:23Z)
• Detached and Interactive Multimodal Learning [17.843121072628477]
  This paper introduces DI-MML, a novel detached MML framework designed to learn complementary information across modalities. It addresses competition by separately training each modality encoder with isolated learning objectives. Experiments conducted on audio-visual, flow-image, and front-rear view datasets show the superior performance of our proposed method.
  arXiv  Detail & Related papers  (2024-07-28T15:38:58Z)
• Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts [54.529880848937104]
  We develop a unified MLLM with the MoE architecture, named Uni-MoE, that can handle a wide array of modalities. Specifically, it features modality-specific encoders with connectors for a unified multimodal representation. We evaluate the instruction-tuned Uni-MoE on a comprehensive set of multimodal datasets.
  arXiv  Detail & Related papers  (2024-05-18T12:16:01Z)
• 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.