Codebook-Guided Cross-Modal Knowledge Distillation for Structurally Heterogeneous Features

arXiv:2609.37243 · cs.CV, cs.AI · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-29

Updated: 2026-10-01

Terminology

• Distillation — 79× in this paper · explained in 2 episodes
• Alignment — 21× in this paper · explained in 1 episode
• Knowledge Distillation — 18× in this paper · explained in 11 episodes
• Quantization — 15× in this paper · explained in 12 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Ablation — 8× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 8× in this paper
• Benchmark — 6× in this paper · explained in 2 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Representation Learning — 4× in this paper · explained in 2 episodes
• Modality Gap — 3× in this paper · explained in 4 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Generalization — 2× in this paper · explained in 13 episodes
• Vector Quantization (VQ) — 2× in this paper · explained in 2 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Alignment Methods — 1× in this paper · explained in 1 episode
• Class-averaged Accuracy — 1× in this paper
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Overall Accuracy — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes

Sources

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Distilling the Knowledge in a Neural Network
• LG-VQ: Language-Guided Codebook Learning
• Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response
• FitNets: Hints for Thin Deep Nets
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Contrastive Representation Distillation

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