# Video Question Answering Using CLIP-Guided Visual-Text Attention

arXiv: https://arxiv.org/abs/2303.03131 · HTML: https://mlsanity.com/paper/2303.03131/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling](https://mlsanity.com/paper/2102.06183/) (2021)
- [TVQA: Localized, Compositional Video Question Answering](https://mlsanity.com/paper/1809.01696/) (2018)
- [HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training](https://mlsanity.com/paper/2005.00200/) (2020)
- [AudioCLIP: Extending CLIP to Image, Text and Audio](https://mlsanity.com/paper/2106.13043/) (2021)
- [Just Ask: Learning to Answer Questions from Millions of Narrated Videos](https://mlsanity.com/paper/2012.00451/) (2020)
- [A Joint Sequence Fusion Model for Video Question Answering and Retrieval](https://mlsanity.com/paper/1808.02559/) (2018)
- [Hierarchical Conditional Relation Networks for Video Question Answering](https://mlsanity.com/paper/2002.10698/) (2020)
- [TVQA+: Spatio-Temporal Grounding for Video Question Answering](https://mlsanity.com/paper/1904.11574/) (2019)
- [Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering](https://mlsanity.com/paper/1904.04357/) (2019)
- [Motion-Appearance Co-Memory Networks for Video Question Answering](https://mlsanity.com/paper/1803.10906/) (2018)
- [Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning](https://mlsanity.com/paper/2003.03186/) (2020)
- [Video as Conditional Graph Hierarchy for Multi-Granular Question Answering](https://mlsanity.com/paper/2112.06197/) (2021)
- [Look Before you Speak: Visually Contextualized Utterances](https://mlsanity.com/paper/2012.05710/) (2020)
- [DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question Answering](https://mlsanity.com/paper/2107.04768/) (2021)

## Built on by (most cited first, 5 of 5)

- [ViQAgent: Zero-Shot Video Question Answering via Agent with Open-Vocabulary Grounding Validation](https://mlsanity.com/paper/2505.15928/) (2025)
- [MMRL++: Parameter-Efficient and Interaction-Aware Representation Learning for Vision-Language Models](https://mlsanity.com/paper/2505.10088/) (2025)
- [Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions](https://mlsanity.com/paper/2308.12562/) (2023)
- [Robust Vision-Language Models via Tensor Decomposition: A Defense Against Adversarial Attacks](https://mlsanity.com/paper/2509.16163/) (2025)
- [MQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuning](https://mlsanity.com/paper/2607.12418/) (2026)

## Abstract (arXiv)

Cross-modal learning of video and text plays a key role in Video Question Answering (VideoQA). In this paper, we propose a visual-text attention mechanism to utilize the Contrastive Language-Image Pre-training (CLIP) trained on lots of general domain language-image pairs to guide the cross-modal learning for VideoQA. Specifically, we first extract video features using a TimeSformer and text features using a BERT from the target application domain, and utilize CLIP to extract a pair of visual-text features from the general-knowledge domain through the domain-specific learning. We then propose a Cross-domain Learning to extract the attention information between visual and linguistic features across the target domain and general domain. The set of CLIP-guided visual-text features are integrated to predict the answer. The proposed method is evaluated on MSVD-QA and MSRVTT-QA datasets, and outperforms state-of-the-art methods.