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Non-target Divergence Hypothesis: Toward Understanding Domain Gaps in Cross-Modal Knowledge Distillation

arXiv 2409.02438 ↗First posted 4 Sep 2024cs.CVcited by 1

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Abstract (arXiv)

Compared to single-modal knowledge distillation, cross-modal knowledge distillation faces more severe challenges due to domain gaps between modalities. Although various methods have proposed various solutions to overcome these challenges, there is still limited research on how domain gaps affect cross-modal knowledge distillation. This paper provides an in-depth analysis and evaluation of this issue. We first introduce the Non-Target Divergence Hypothesis (NTDH) to reveal the impact of domain gaps on cross-modal knowledge distillation. Our key finding is that domain gaps between modalities lead to distribution differences in non-target classes, and the smaller these differences, the better the performance of cross-modal knowledge distillation. Subsequently, based on Vapnik-Chervonenkis (VC) theory, we derive the upper and lower bounds of the approximation error for cross-modal knowledge distillation, thereby theoretically validating the NTDH. Finally, experiments on five cross-modal datasets further confirm the validity, generalisability, and applicability of the NTDH.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 57 references that are in the map. First posted on arXiv 4 Sep 2024.

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27 of 57 references.

References · 27

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2014FitNets: Hints for Thin Deep Netscited by 1,619
  3. 2020Knowledge Distillation: A Surveycited by 1,578
  4. 2019Relational Knowledge Distillationcited by 873
  5. 2019Contrastive Representation Distillationcited by 734
  6. 2019Improved Knowledge Distillation via Teacher Assistantcited by 567
  7. 2019Similarity-Preserving Knowledge Distillationcited by 507
  8. 2022Decoupled Knowledge Distillationcited by 380
  9. 2019On the Efficacy of Knowledge Distillationcited by 377
  10. 2020Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlookscited by 312
  11. 2021Towards Understanding Knowledge Distillationcited by 158
  12. 20222DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Cloudscited by 157
15 more
  1. 2020Anisotropic Convolutional Networks for 3D Semantic Scene Completioncited by 126
  2. 2020Understanding and Improving Knowledge Distillationcited by 83
  3. 2023Decoupled Multimodal Distilling for Emotion Recognitioncited by 76
  4. 2022BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detectioncited by 66
  5. 2021EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentationcited by 59
  6. 2022The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillationcited by 44
  7. 2023Learnable Cross-modal Knowledge Distillation for Multi-modal Learning with Missing Modalitycited by 44
  8. 2021Co-advise: Cross Inductive Bias Distillationcited by 40
  9. 2021Learning an Augmented RGB Representation with Cross-Modal Knowledge Distillation for Action Detectioncited by 32
  10. 2020Feature Importance Ranking for Deep Learningcited by 31
  11. 2022XKD: Cross-modal Knowledge Distillation with Domain Alignment for Video Representation Learningcited by 30
  12. 2023Decomposed Cross-modal Distillation for RGB-based Temporal Action Detectioncited by 12
  13. 2023Enhanced Multimodal Representation Learning with Cross-modal KDcited by 11
  14. 2024Vision-Language Meets the Skeleton: Progressively Distillation with Cross-Modal Knowledge for 3D Action Representation Learningcited by 11
  15. 2024LabelDistill: Label-guided Cross-modal Knowledge Distillation for Camera-based 3D Object Detectioncited by 9

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