# Contextual AD Narration with Interleaved Multimodal Sequence

arXiv: https://arxiv.org/abs/2403.12922 · HTML: https://mlsanity.com/paper/2403.12922/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 56 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Builds on (most cited first, 30 of 33)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval](https://mlsanity.com/paper/2104.00650/) (2021)
- [VideoChat: Chat-Centric Video Understanding](https://mlsanity.com/paper/2305.06355/) (2023)
- [Dense-Captioning Events in Videos](https://mlsanity.com/paper/1705.00754/) (2017)
- [ClipCap: CLIP Prefix for Image Captioning](https://mlsanity.com/paper/2111.09734/) (2021)
- [DreamLLM: Synergistic Multimodal Comprehension and Creation](https://mlsanity.com/paper/2309.11499/) (2023)
- [MovieNet: A Holistic Dataset for Movie Understanding](https://mlsanity.com/paper/2007.10937/) (2020)
- [SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning](https://mlsanity.com/paper/2111.13196/) (2021)
- [Emu: Generative Pretraining in Multimodality](https://mlsanity.com/paper/2307.05222/) (2023)
- [CM3: A Causal Masked Multimodal Model of the Internet](https://mlsanity.com/paper/2201.07520/) (2022)
- [MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions](https://mlsanity.com/paper/2112.00431/) (2021)
- [MM-VID: Advancing Video Understanding with GPT-4V(ision)](https://mlsanity.com/paper/2310.19773/) (2023)
- [Streamlined Dense Video Captioning](https://mlsanity.com/paper/1904.03870/) (2019)
- [AutoAD: Movie Description in Context](https://mlsanity.com/paper/2303.16899/) (2023)
- [AutoAD II: The Sequel -- Who, When, and What in Movie Audio Description](https://mlsanity.com/paper/2310.06838/) (2023)
- [MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learning](https://mlsanity.com/paper/2311.17435/) (2023)
- [Video ChatCaptioner: Towards Enriched Spatiotemporal Descriptions](https://mlsanity.com/paper/2304.04227/) (2023)
- [Watch, Listen and Tell: Multi-modal Weakly Supervised Dense Event Captioning](https://mlsanity.com/paper/1909.09944/) (2019)
- [Learning Interactions and Relationships between Movie Characters](https://mlsanity.com/paper/2003.13158/) (2020)
- [Rescribe: Authoring and Automatically Editing Audio Descriptions](https://mlsanity.com/paper/2010.03667/) (2020)

## Built on by (most cited first, 14 of 14)

- [AutoAD-Zero: A Training-Free Framework for Zero-Shot Audio Description](https://mlsanity.com/paper/2407.15850/) (2024)
- [Shot-by-Shot: Film-Grammar-Aware Training-Free Audio Description Generation](https://mlsanity.com/paper/2504.01020/) (2025)
- [DistinctAD: Distinctive Audio Description Generation in Contexts](https://mlsanity.com/paper/2411.18180/) (2024)
- [Face-Human-Bench: A Comprehensive Benchmark of Face and Human Understanding for Multi-modal Assistants](https://mlsanity.com/paper/2501.01243/) (2025)
- [UniM: A Unified Any-to-Any Interleaved Multimodal Benchmark](https://mlsanity.com/paper/2603.05075/) (2026)
- [Character-Centric Understanding of Animated Movies](https://mlsanity.com/paper/2509.12204/) (2025)
- [MCAD: Multimodal Context-Aware Audio Description Generation For Soccer](https://mlsanity.com/paper/2511.09448/) (2025)
- [REFRAMED: Towards Realistic Audio Description Generation for Movies](https://mlsanity.com/paper/2608.09765/) (2026)
- [Integrating Video and Text: A Balanced Approach to Multimodal Summary Generation and Evaluation](https://mlsanity.com/paper/2505.06594/) (2025)
- [READ More than What You See: Reinforcement Learning for Accurate and Coherent Audio Description Generations](https://mlsanity.com/paper/2606.22766/) (2026)
- [Andha-Dhun: A First Look at Audio Descriptions in Hindi](https://mlsanity.com/paper/2607.06457/) (2026)
- [StrAD: A Streaming Method and Benchmark for Audio Description Generation for Long-form Videos](https://mlsanity.com/paper/2608.12549/) (2026)
- [TAKE 85: Testing Audiovisual filmmaKer's intEnt across 85 Hours of Film](https://mlsanity.com/paper/2608.30068/) (2026)
- [What, When, and How: Audio Description as Constrained Global Optimization](https://mlsanity.com/paper/2609.30121/) (2026)

## Abstract (arXiv)

The Audio Description (AD) task aims to generate descriptions of visual elements for visually impaired individuals to help them access long-form video content, like movies. With video feature, text, character bank and context information as inputs, the generated ADs are able to correspond to the characters by name and provide reasonable, contextual descriptions to help audience understand the storyline of movie. To achieve this goal, we propose to leverage pre-trained foundation models through a simple and unified framework to generate ADs with interleaved multimodal sequence as input, termed as Uni-AD. To enhance the alignment of features across various modalities with finer granularity, we introduce a simple and lightweight module that maps video features into the textual feature space. Moreover, we also propose a character-refinement module to provide more precise information by identifying the main characters who play more significant roles in the video context. With these unique designs, we further incorporate contextual information and a contrastive loss into our architecture to generate smoother and more contextually appropriate ADs. Experiments on multiple AD datasets show that Uni-AD performs well on AD generation, which demonstrates the effectiveness of our approach. Our code is available at: https://github.com/ant-research/UniAD.