# VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer

arXiv: https://arxiv.org/abs/2107.02681 · HTML: https://mlsanity.com/paper/2107.02681/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal distillation, one of 12 directions in Knowledge distillation.
- Builds on 65 of its 85 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Placed in

- Knowledge distillation / [Multimodal distillation](https://mlsanity.com/distill/multimodal-distillation/)

## Builds on (most cited first, 30 of 65)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)

## Built on by (most cited first, 22 of 22)

- [Vision-Language Pre-training: Basics, Recent Advances, and Future Trends](https://mlsanity.com/paper/2210.09263/) (2022)
- [Lifelong Learning of Large Language Model based Agents: A Roadmap](https://mlsanity.com/paper/2501.07278/) (2025)
- [VALHALLA: Visual Hallucination for Machine Translation](https://mlsanity.com/paper/2206.00100/) (2022)
- [MCSE: Multimodal Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2204.10931/) (2022)
- [Visually-Augmented Language Modeling](https://mlsanity.com/paper/2205.10178/) (2022)
- [KDMCSE: Knowledge Distillation Multimodal Sentence Embeddings with Adaptive Angular margin Contrastive Learning](https://mlsanity.com/paper/2403.17486/) (2024)
- [Sentence-Level or Token-Level? A Comprehensive Study on Knowledge Distillation](https://mlsanity.com/paper/2404.14827/) (2024)
- [Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer](https://mlsanity.com/paper/2203.07519/) (2022)
- [Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention](https://mlsanity.com/paper/2211.11701/) (2022)
- [Learning to Imagine: Visually-Augmented Natural Language Generation](https://mlsanity.com/paper/2305.16944/) (2023)
- [Text-Derived Knowledge Helps Vision: A Simple Cross-modal Distillation for Video-based Action Anticipation](https://mlsanity.com/paper/2210.05991/) (2022)
- [Learning to Unify Audio, Visual and Text for Audio-Enhanced Multilingual Visual Answer Localization](https://mlsanity.com/paper/2411.02851/) (2024)
- [Localization vs. Semantics: Visual Representations in Unimodal and Multimodal Models](https://mlsanity.com/paper/2212.00281/) (2022)
- [Towards Versatile and Efficient Visual Knowledge Integration into Pre-trained Language Models with Cross-Modal Adapters](https://mlsanity.com/paper/2305.07358/) (2023)
- [DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning](https://mlsanity.com/paper/2506.21096/) (2025)
- [Teach me with a Whisper: Enhancing Large Language Models for Analyzing Spoken Transcripts using Speech Embeddings](https://mlsanity.com/paper/2311.07014/) (2023)
- [WinoViz: Probing Visual Properties of Objects Under Different States](https://mlsanity.com/paper/2402.13584/) (2024)
- [LaMI: Augmenting Large Language Models via Late Multi-Image Fusion](https://mlsanity.com/paper/2406.13621/) (2024)
- [Improving the Efficiency of Visually Augmented Language Models](https://mlsanity.com/paper/2409.11148/) (2024)
- [If you can describe it, they can see it: Cross-Modal Learning of Visual Concepts from Textual Descriptions](https://mlsanity.com/paper/2411.15611/) (2024)
- [From Images to Words: Efficient Cross-Modal Knowledge Distillation to Language Models from Black-box Teachers](https://mlsanity.com/paper/2603.10877/) (2026)
- [Evolving Knowledge Distillation for Lightweight Neural Machine Translation](https://mlsanity.com/paper/2605.09924/) (2026)

## Abstract (arXiv)

Since visual perception can give rich information beyond text descriptions for world understanding, there has been increasing interest in leveraging visual grounding for language learning. Recently, vokenization (Tan and Bansal, 2020) has attracted attention by using the predictions of a text-to-image retrieval model as labels for language model supervision. Despite its success, the method suffers from approximation error of using finite image labels and the lack of vocabulary diversity of a small image-text dataset. To overcome these limitations, we present VidLanKD, a video-language knowledge distillation method for improving language understanding. We train a multi-modal teacher model on a video-text dataset, and then transfer its knowledge to a student language model with a text dataset. To avoid approximation error, we propose to use different knowledge distillation objectives. In addition, the use of a large-scale video-text dataset helps learn diverse and richer vocabularies. In our experiments, VidLanKD achieves consistent improvements over text-only language models and vokenization models, on several downstream language understanding tasks including GLUE, SQuAD, and SWAG. We also demonstrate the improved world knowledge, physical reasoning, and temporal reasoning capabilities of our model by evaluating on the GLUE-diagnostics, PIQA, and TRACIE datasets. Lastly, we present comprehensive ablation studies as well as visualizations of the learned text-to-video grounding results of our teacher and student language models. Our code and models are available at: https://github.com/zinengtang/VidLanKD