The Modality Focusing Hypothesis: On the Blink of Multimodal Knowledge
Distillation
- URL: http://arxiv.org/abs/2206.06487v1
- Date: Mon, 13 Jun 2022 21:34:21 GMT
- Title: The Modality Focusing Hypothesis: On the Blink of Multimodal Knowledge
Distillation
- Authors: Zihui Xue, Zhengqi Gao, Sucheng Ren, Hang Zhao
- Abstract summary: Multimodal knowledge distillation extends traditional knowledge distillation to the area of multimodal learning.
One common practice is to adopt a well-performed multimodal network as the teacher in the hope that it can transfer its full knowledge to a unimodal student for performance improvement.
- Score: 16.399589194973814
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multimodal knowledge distillation (KD) extends traditional knowledge
distillation to the area of multimodal learning. One common practice is to
adopt a well-performed multimodal network as the teacher in the hope that it
can transfer its full knowledge to a unimodal student for performance
improvement. In this paper, we investigate the efficacy of multimodal KD. We
begin by providing two failure cases of it and demonstrate that KD is not a
universal cure in multimodal knowledge transfer. We present the modality Venn
diagram to understand modality relationships and the modality focusing
hypothesis revealing the decisive factor in the efficacy of multimodal KD.
Experimental results on 6 multimodal datasets help justify our hypothesis,
diagnose failure cases, and point directions to improve distillation
performance.
Related papers
- Information-Theoretic Criteria for Knowledge Distillation in Multimodal Learning [7.255275023242901]
Cross-modal knowledge distillation (KD) is a technique where "teacher" modalities transfer information to weaker "student" modalities during model training to improve performance.<n>Despite successes across various applications, cross-modal KD does not always result in improved outcomes, primarily due to a limited theoretical understanding that could inform practice.<n>We propose that cross-modal KD is effective when the mutual information between teacher and student representations exceeds the mutual information between the student representation and the labels.<n>Our study establishes a novel theoretical framework for understanding cross-modal KD and offers practical guidelines based on the CCH criterion to select optimal teacher modalities for improving the performance of
arXiv Detail & Related papers (2025-10-15T06:10:10Z) - Continual Multimodal Contrastive Learning [70.60542106731813]
Multimodal contrastive learning (MCL) advances in aligning different modalities and generating multimodal representations in a joint space.
However, a critical yet often overlooked challenge remains: multimodal data is rarely collected in a single process, and training from scratch is computationally expensive.
In this paper, we formulate CMCL through two specialized principles of stability and plasticity.
We theoretically derive a novel optimization-based method, which projects updated gradients from dual sides onto subspaces where any gradient is prevented from interfering with the previously learned knowledge.
arXiv Detail & Related papers (2025-03-19T07:57:08Z) - MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks [50.98856172702256]
We propose the Modality-INformed knowledge Distillation (MIND) framework, a multimodal model compression approach.
MIND transfers knowledge from ensembles of pre-trained deep neural networks of varying sizes into a smaller multimodal student.
We evaluate MIND on binary and multilabel clinical prediction tasks using time series data and chest X-ray images.
arXiv Detail & Related papers (2025-02-03T08:50:00Z) - Multimodal Industrial Anomaly Detection by Crossmodal Reverse Distillation [15.89869857998053]
We propose Crossmodal Reverse Distillation (CRD) based on Multi-branch design to realize Multimodal Industrial AD.
By assigning independent branches to each modality, our method enables finer detection of anomalies within each modality.
Our method achieves state-of-the-art performance in multimodal anomaly detection and localization.
arXiv Detail & Related papers (2024-12-12T05:26:50Z) - Enhancing Knowledge Distillation of Large Language Models through Efficient Multi-Modal Distribution Alignment [10.104085497265004]
We propose Ranking Loss based Knowledge Distillation (RLKD), which encourages consistency of peak predictions between the teacher and student models.
Our method enables the student model to better learn the multi-modal distributions of the teacher model, leading to a significant performance improvement in various downstream tasks.
arXiv Detail & Related papers (2024-09-19T08:06:42Z) - DisCoM-KD: Cross-Modal Knowledge Distillation via Disentanglement Representation and Adversarial Learning [3.763772992906958]
Cross-modal knowledge distillation (CMKD) refers to the scenario in which a learning framework must handle training and test data that exhibit a modality mismatch.
DisCoM-KD (Disentanglement-learning based Cross-Modal Knowledge Distillation) explicitly models different types of per-modality information.
arXiv Detail & Related papers (2024-08-05T13:44:15Z) - Beyond Unimodal Learning: The Importance of Integrating Multiple Modalities for Lifelong Learning [23.035725779568587]
We study the role and interactions of multiple modalities in mitigating forgetting in deep neural networks (DNNs)
Our findings demonstrate that leveraging multiple views and complementary information from multiple modalities enables the model to learn more accurate and robust representations.
We propose a method for integrating and aligning the information from different modalities by utilizing the relational structural similarities between the data points in each modality.
arXiv Detail & Related papers (2024-05-04T22:02:58Z) - 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) - CMD: Self-supervised 3D Action Representation Learning with Cross-modal
Mutual Distillation [130.08432609780374]
In 3D action recognition, there exists rich complementary information between skeleton modalities.
We propose a new Cross-modal Mutual Distillation (CMD) framework with the following designs.
Our approach outperforms existing self-supervised methods and sets a series of new records.
arXiv Detail & Related papers (2022-08-26T06:06:09Z) - Modality Competition: What Makes Joint Training of Multi-modal Network
Fail in Deep Learning? (Provably) [75.38159612828362]
It has been observed that the best uni-modal network outperforms the jointly trained multi-modal network.
This work provides a theoretical explanation for the emergence of such performance gap in neural networks for the prevalent joint training framework.
arXiv Detail & Related papers (2022-03-23T06:21:53Z) - Channel Exchanging Networks for Multimodal and Multitask Dense Image
Prediction [125.18248926508045]
We propose Channel-Exchanging-Network (CEN) which is self-adaptive, parameter-free, and more importantly, applicable for both multimodal fusion and multitask learning.
CEN dynamically exchanges channels betweenworks of different modalities.
For the application of dense image prediction, the validity of CEN is tested by four different scenarios.
arXiv Detail & Related papers (2021-12-04T05:47:54Z) - Multimodal Knowledge Expansion [14.332957885505547]
We propose a knowledge distillation-based framework to utilize multimodal data without requiring labels.
We show that a multimodal student model consistently denoises pseudo labels and generalizes better than its teacher.
arXiv Detail & Related papers (2021-03-26T12:32:07Z) - Collaborative Teacher-Student Learning via Multiple Knowledge Transfer [79.45526596053728]
We propose a collaborative teacher-student learning via multiple knowledge transfer (CTSL-MKT)
It allows multiple students learn knowledge from both individual instances and instance relations in a collaborative way.
The experiments and ablation studies on four image datasets demonstrate that the proposed CTSL-MKT significantly outperforms the state-of-the-art KD methods.
arXiv Detail & Related papers (2021-01-21T07:17:04Z) - Modality-specific Distillation [30.190082262375395]
We propose modality-specific distillation (MSD) to effectively transfer knowledge from a teacher on multimodal datasets.
Our idea aims at mimicking a teacher's modality-specific predictions by introducing an auxiliary loss term for each modality.
Because each modality has different importance for predictions, we also propose weighting approaches for the auxiliary losses.
arXiv Detail & Related papers (2021-01-06T05:45:07Z) - Unpaired Multi-modal Segmentation via Knowledge Distillation [77.39798870702174]
We propose a novel learning scheme for unpaired cross-modality image segmentation.
In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI.
We have extensively validated our approach on two multi-class segmentation problems.
arXiv Detail & Related papers (2020-01-06T20:03:17Z)
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.