Interpretation on Multi-modal Visual Fusion

• URL: http://arxiv.org/abs/2308.10019v1
• Date: Sat, 19 Aug 2023 14:01:04 GMT
• Title: Interpretation on Multi-modal Visual Fusion
• Authors: Hao Chen, Haoran Zhou, Yongjian Deng
• Abstract summary: We present an analytical framework and a novel metric to shed light on the interpretation of the multimodal vision community. We investigate the consistency and speciality of representations across modalities, evolution rules within each modality, and the collaboration logic used when optimizing a multi-modality model.
• Score: 10.045591415286516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present an analytical framework and a novel metric to shed light on the interpretation of the multimodal vision community. Our approach involves measuring the proposed semantic variance and feature similarity across modalities and levels, and conducting semantic and quantitative analyses through comprehensive experiments. Specifically, we investigate the consistency and speciality of representations across modalities, evolution rules within each modality, and the collaboration logic used when optimizing a multi-modality model. Our studies reveal several important findings, such as the discrepancy in cross-modal features and the hybrid multi-modal cooperation rule, which highlights consistency and speciality simultaneously for complementary inference. Through our dissection and findings on multi-modal fusion, we facilitate a rethinking of the reasonability and necessity of popular multi-modal vision fusion strategies. Furthermore, our work lays the foundation for designing a trustworthy and universal multi-modal fusion model for a variety of tasks in the future.

Related papers

• Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey [124.23247710880008]
  multimodal CoT (MCoT) reasoning has recently garnered significant research attention. Existing MCoT studies design various methodologies to address the challenges of image, video, speech, audio, 3D, and structured data. We present the first systematic survey of MCoT reasoning, elucidating the relevant foundational concepts and definitions.
  arXiv  Detail & Related papers  (2025-03-16T18:39:13Z)
• A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models [74.48084001058672]
  The rise of foundation models has transformed machine learning research. multimodal foundation models (MMFMs) pose unique interpretability challenges beyond unimodal frameworks. This survey explores two key aspects: (1) the adaptation of LLM interpretability methods to multimodal models and (2) understanding the mechanistic differences between unimodal language models and crossmodal systems.
  arXiv  Detail & Related papers  (2025-02-22T20:55:26Z)
• Progressive Multimodal Reasoning via Active Retrieval [64.74746997923967]
  Multi-step multimodal reasoning tasks pose significant challenges for large language models (MLLMs) We propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs. We show that AR-MCTS can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.
  arXiv  Detail & Related papers  (2024-12-19T13:25:39Z)
• Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks [49.84182981950623]
  Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task.<n>It requires not only the effective extraction of features from both modalities and robust fusion strategies, but also the ability to address issues such as spectral discrepancies.<n>We introduce an efficient and easily deployable multispectral object detection framework that can seamlessly optimize high-performing single-modality models.
  arXiv  Detail & Related papers  (2024-11-27T12:18:39Z)
• On the Comparison between Multi-modal and Single-modal Contrastive Learning [50.74988548106031]
  We introduce a theoretical foundation for understanding the differences between multi-modal and single-modal contrastive learning. We identify the critical factor, which is the signal-to-noise ratio (SNR), that impacts the generalizability in downstream tasks of both multi-modal and single-modal contrastive learning. Our analysis provides a unified framework that can characterize the optimization and generalization of both single-modal and multi-modal contrastive learning.
  arXiv  Detail & Related papers  (2024-11-05T06:21:17Z)
• Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework [58.362064122489166]
  This paper introduces the Cross-modal Few-Shot Learning task, which aims to recognize instances across multiple modalities while relying on scarce labeled data.<n>We propose a Generative Transfer Learning framework by simulating how humans abstract and generalize concepts.<n>We show that the GTL achieves state-of-the-art performance across seven multi-modal datasets across RGB-Sketch, RGB-Infrared, and RGB-Depth.
  arXiv  Detail & Related papers  (2024-10-14T16:09:38Z)
• What to align in multimodal contrastive learning? [7.7439394183358745]
  We introduce Contrastive MultiModal learning strategy that enables the communication between modalities in a single multimodal space. Our theoretical analysis shows that shared, synergistic and unique terms of information naturally emerge from this formulation, allowing us to estimate multimodal interactions beyond redundancy. In the latter, CoMM learns complex multimodal interactions and achieves state-of-the-art results on the six multimodal benchmarks.
  arXiv  Detail & Related papers  (2024-09-11T16:42:22Z)
• Asynchronous Multimodal Video Sequence Fusion via Learning Modality-Exclusive and -Agnostic Representations [19.731611716111566]
  We propose a Multimodal fusion approach for learning modality-Exclusive and modality-Agnostic representations. We introduce a predictive self-attention module to capture reliable context dynamics within modalities. A hierarchical cross-modal attention module is designed to explore valuable element correlations among modalities. A double-discriminator strategy is presented to ensure the production of distinct representations in an adversarial manner.
  arXiv  Detail & Related papers  (2024-07-06T04:36:48Z)
• Confidence-aware multi-modality learning for eye disease screening [58.861421804458395]
  We propose a novel multi-modality evidential fusion pipeline for eye disease screening. It provides a measure of confidence for each modality and elegantly integrates the multi-modality information. Experimental results on both public and internal datasets demonstrate that our model excels in robustness.
  arXiv  Detail & Related papers  (2024-05-28T13:27:30Z)
• Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning [51.80447197290866]
  Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning. Existing methods focus on crafting elegant entity-wise multi-modal fusion strategies. We introduce a novel framework with Mixture of Modality Knowledge experts (MoMoK) to learn adaptive multi-modal entity representations.
  arXiv  Detail & Related papers  (2024-05-27T06:36:17Z)
• Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning [80.44084021062105]
  We propose a novel latent partial causal model for multimodal data, featuring two latent coupled variables, connected by an undirected edge, to represent the transfer of knowledge across modalities.<n>Under specific statistical assumptions, we establish an identifiability result, demonstrating that representations learned by multimodal contrastive learning correspond to the latent coupled variables up to a trivial transformation.<n>Experiments on a pre-trained CLIP model embodies disentangled representations, enabling few-shot learning and improving domain generalization across diverse real-world datasets.
  arXiv  Detail & Related papers  (2024-02-09T07:18:06Z)
• Joint Self-Supervised and Supervised Contrastive Learning for Multimodal MRI Data: Towards Predicting Abnormal Neurodevelopment [5.771221868064265]
  We present a novel joint self-supervised and supervised contrastive learning method to learn the robust latent feature representation from multimodal MRI data. Our method has the capability to facilitate computer-aided diagnosis within clinical practice, harnessing the power of multimodal data.
  arXiv  Detail & Related papers  (2023-12-22T21:05:51Z)
• 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)
• MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks [56.60050181186531]
  We introduce MM-BigBench, which incorporates a diverse range of metrics to offer an extensive evaluation of the performance of various models and instructions. Our paper evaluates a total of 20 language models (14 MLLMs) on 14 multimodal datasets spanning 6 tasks, with 10 instructions for each task, and derives novel insights.
  arXiv  Detail & Related papers  (2023-10-13T11:57:04Z)
• Decoupling Common and Unique Representations for Multimodal Self-supervised Learning [22.12729786091061]
  We propose Decoupling Common and Unique Representations (DeCUR), a simple yet effective method for multimodal self-supervised learning. By distinguishing inter- and intra-modal embeddings through multimodal redundancy reduction, DeCUR can integrate complementary information across different modalities.
  arXiv  Detail & Related papers  (2023-09-11T08:35:23Z)
• Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method [3.0903319879656084]
  This paper introduces an innovative approach to feature alignment that revolutionizes the fusion of multimodal information. Our method employs a novel iterative process of telescopic displacement and expansion of feature representations across different modalities, culminating in a coherent unified representation within a shared feature space.
  arXiv  Detail & Related papers  (2023-06-29T13:49:06Z)
• Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework [89.8609061423685]
  We propose an information-theoretic approach to quantify the degree of redundancy, uniqueness, and synergy relating input modalities with an output task. To validate PID estimation, we conduct extensive experiments on both synthetic datasets where the PID is known and on large-scale multimodal benchmarks. We demonstrate their usefulness in (1) quantifying interactions within multimodal datasets, (2) quantifying interactions captured by multimodal models, (3) principled approaches for model selection, and (4) three real-world case studies.
  arXiv  Detail & Related papers  (2023-02-23T18:59:05Z)
• SHAPE: An Unified Approach to Evaluate the Contribution and Cooperation of Individual Modalities [7.9602600629569285]
  We use bf SHapley vbf Alue-based bf PErceptual (SHAPE) scores to measure the marginal contribution of individual modalities and the degree of cooperation across modalities. Our experiments suggest that for some tasks where different modalities are complementary, the multi-modal models still tend to use the dominant modality alone. We hope our scores can help improve the understanding of how the present multi-modal models operate on different modalities and encourage more sophisticated methods of integrating multiple modalities.
  arXiv  Detail & Related papers  (2022-04-30T16:35:40Z)
• Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection [7.062058947498447]
  salient visual cues appear in various scales and resolutions of RGB images due to semantic gaps at different feature levels. Similar salient patterns are available in cross-modal depth images as well as multi-scale versions. We devise attention based fusion module (ABF) to address on cross-modal correlation.
  arXiv  Detail & Related papers  (2022-01-24T10:33:00Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• Siamese Network for RGB-D Salient Object Detection and Beyond [113.30063105890041]
  A novel framework is proposed to learn from both RGB and depth inputs through a shared network backbone. Comprehensive experiments using five popular metrics show that the designed framework yields a robust RGB-D saliency detector. We also link JL-DCF to the RGB-D semantic segmentation field, showing its capability of outperforming several semantic segmentation models.
  arXiv  Detail & Related papers  (2020-08-26T06:01:05Z)
• Skeleton Focused Human Activity Recognition in RGB Video [11.521107108725188]
  We propose a multimodal feature fusion model that utilizes both skeleton and RGB modalities to infer human activity. The model could be either individually or uniformly trained by the back-propagation algorithm in an end-to-end manner.
  arXiv  Detail & Related papers  (2020-04-29T06:40:42Z)

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.