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DANTE-AD: Dual-Vision Attention Network for Long-Term Audio Description

Adrienne Deganutti, Simon Hadfield, Andrew Gilbert

arXiv:2503.24096v1cs.CV

TL;DR

Long-form video description still lacks coherent narrative context, although audio description can help vision-impaired audiences access visual storytelling. DANTE-AD sequentially fuses frame- and scene-level representations, and the authors report that it outperforms existing audio-description techniques on real-world movie clips.

  • Problem

    Long-form video description needs coherent visual context across scenes, while prior frame-only methods provide limited scene context and audio description remains important for media accessibility.

    Source: §1 · p. 1, §5 · p. 8

  • Method

    DANTE-AD sequentially fuses complementary frame- and scene-level visual embeddings to ground fine-grained details in long-term context.

    Source: §1 · p. 2

  • Results

    DANTE-AD significantly outperforms existing audio-description techniques in evaluations on real-world movie clips.

    Source: §5 · p. 8

  • Takeaways & Limitations

    The findings support combining spatial and temporal visual representations to bridge object-centric captioning and holistic audio-description storytelling.

    Source: §5 · p. 8

    Future work may refine long-term scene understanding and adaptive attention, and incorporate additional modalities such as audio and subtitles.

    Source: §5 · p. 8

Abstract

from arXiv · show

Audio Description is a narrated commentary designed to aid vision-impaired audiences in perceiving key visual elements in a video. While short-form video understanding has advanced rapidly, a solution for maintaining coherent long-term visual storytelling remains unresolved. Existing methods rely solely on frame-level embeddings, effectively describing object-based content but lacking contextual information across scenes. We introduce DANTE-AD, an enhanced video description model leveraging a dual-vision Transformer-based architecture to address this gap. DANTE-AD sequentially fuses both frame and scene level embeddings to improve long-term contextual understanding. We propose a novel, state-of-the-art method for sequential cross-attention to achieve contextual grounding for fine-grained audio description generation. Evaluated on a broad range of key scenes from well-known movie clips, DANTE-AD outperforms existing methods across traditional NLP metrics and LLM-based evaluations.

1. Introduction

Audio description can restore visual storytelling for blind and low-vision audiences, but manual production is resource-intensive and long-form video captioning struggles to maintain coherent, context-aware narratives. DANTE-AD combines frame- and scene-level representations through sequential fusion and is evaluated on long-term film clips.

1. Introduction

  • Manual audio description requires skilled professionals and substantial time, leaving many videos without description or with limited coverage.The paper frames automation as a crucial accessibility challenge.Source: §1 · p. 1
  • Long-form video captioning struggles with concurrent events and lacks the detailed, context-aware descriptions needed to capture a broader narrative.The paper identifies this gap as especially important for films and television programmes.Source: §1 · pp. 1–2
  • DANTE-AD combines complementary frame-level spatial and scene-level temporal representations through sequential fusion that retains scene context when querying frame details.The authors present this as a way to improve detailed, context-aware descriptions over extended sequences.Source: §1 · p. 2
  • The paper reports extensive evaluation on real long-term film clips using realistic real-world metrics.The passage does not specify the metrics or comparative results.Source: §1 · p. 2

2. Related Works

Prior work on video captioning, visual storytelling, multimodal inputs, and long-term modeling informs DANTE-AD’s focus on coherent narrative context. The paper positions dual visual embeddings as an approach to long-term scene understanding.

2. Related Works

  • Conventional video captioning methods often struggle to capture narrative structure and long-term dependencies needed for audio description.The cited work spans sequence-to-sequence attention, transformers, reinforcement learning, and retrieval-augmented approaches.Source: §2 · p. 2

2.1. Visual Storytelling

  • Audio description requires cohesion across frames and engaging, logically structured narration; prior work addresses redundancy and character identification, while DANTE-AD focuses on scene understanding.The paper connects visual storytelling to areas including navigation, game generation, and social-media analysis.Source: §2.1 · p. 2

2.2. Multimodal Video Captioning

  • Prior multimodal captioning adds inputs such as audio, subtitles, motion, memory, or emotion; DANTE-AD instead adds a context-focused visual modality alongside frame input and text references.The cited examples include subtitle-based moment localization and movie-book alignment.Source: §2.2 · pp. 2–3

2.3. Long-Term Modelling

  • Short-form captioning often lacks global coherence and long-term structure; DANTE-AD uses dual visual embeddings to target scene understanding as video duration increases.The passage also notes approaches using autoregressive decoding, memory clustering, and recursive captioning for long-term memory.Source: §2.3 · p. 3

3. Model Architecture

DANTE-AD combines parallel frame- and scene-level visual branches, sequentially fuses their representations with AD text embeddings, and decodes the result into natural language. The architecture uses pretrained components and selectively trains projection layers and the dual-vision attention network.

3. Model Architecture

  • The architecture has parallel frame and scene feature branches whose outputs are sequentially integrated by a dual-vision Transformer before a frozen language model decodes them.The Transformer uses three layers, each with self-attention, cross-attention, and a feed-forward network.Source: §3 · p. 3, §3.3 · p. 4

3.1. Frame-Level Visual Branch

  • Frame-level features come from per-frame EVA-CLIP embeddings, Q-Former cross-attention for spatial information, and positional embeddings that encode relative frame positions.The position-encoded queries are flattened and processed by the Video Q-Former to form a global video representation.Source: §3.1 · p. 4

3.2. Scene-Level Visual Branch

  • The scene branch uses Side4Video to produce spatial-temporal features, applies global average pooling for a scene-level representation, and projects it to LLaMA2’s embedding dimension.Side4Video runs a lightweight side network in parallel with CLIP-ViT-B/16.Source: §3.2 · p. 4

3.3. Dual-Vision Transformer

  • The dual-vision Transformer attends first to frame embeddings for fine-grained details, then to scene embeddings for broader context, while predicting AD tokens from preceding ground-truth tokens during training.At inference, it starts with [BOS] and recursively feeds its predictions back until the sequence is complete.Source: §3.3 · pp. 4–5

3.4. Language Model

  • The frozen LLaMA2-7B decoder receives projected dual-vision outputs, with [BOS] and [EOS] embeddings marking the start and end of generated text.The decoder remains frozen to preserve generalisation capabilities and reduce computational overhead.Source: §3.4 · p. 5

3.5. Training Details

  • Training subsamples film sequences to 8 frames and updates only the frame- and scene-projection layers and dual-vision attention network, keeping other components frozen.Context-aware fine-tuning runs for 2 epochs; offline visual embeddings let training fit on one RTX-4090 GPU with 24GB memory.Source: §3.5 · p. 5

4. Experiments

Experiments evaluate DANTE-AD on CMD-AD using traditional NLP and LLM-based metrics. The reported results cover comparisons with prior methods, embedding order, caption length, and qualitative output.

4.1. Datasets

  • CMD-AD contains approximately two-minute key scenes from well-known films, with human-generated descriptions; processing reduced the dataset from about 101k to 96k segments.The processed dataset statistics are compared with the original CMD-AD in Table 2.Source: §4.1 · pp. 5–6, Table 2 · p. 6

4.2. Evaluation Metrics

  • Evaluation combines CIDEr and Recall@k/N with LLM-AD-Eval scores from LLaMA2-7B-chat and GPT-3.5-turbo.The LLM metric uses a 0–5 similarity score converted to a percentage; the authors note ambiguity in subjective score interpretation.Source: §4.2 · p. 6

4.3. Quantitative Results

  • DANTE-AD outperforms prior AD methods on CIDEr and LLM-AD-Eval with LLaMA2-7B-chat on CMD-AD.Table 1 reports comparisons on CMD-AD; the supplied text does not give its individual scores.Source: §4.3 · p. 6, Table 1 · p. 6
  • DANTE-AD descriptions average longer than AutoAD-III [11] descriptions, and the authors attribute this to additional scene-level context.The paper links the longer descriptions to greater detail and compares an example in Figure 6.Source: §4.3 · pp. 6–7, Figure 4 · p. 7
  • Frame-level information followed by scene-level information improves contextual precision, while the reverse order produces more elaborate descriptions.The paper attributes the former to CIDEr results and illustrates the latter in qualitative examples.Source: §4.3 · p. 7, Table 3 · p. 7, Figure 5 · p. 7
  • Concatenation produces the longest captions, which can exceed the video’s visual content; its CIDEr score is 21 versus 28.9 for sequential fusion.The authors note that sequential outputs can describe correct visual content from a different perspective than the ground truth.Source: §4.3 · p. 7, Figure 7 · p. 8

4.4. Qualitative Results

  • Qualitative examples show DANTE-AD outputs that are neither too brief nor verbose, while LLM-based similarity scores can vary despite qualitative alignment with the ground truth.The examples also underscore the importance of ordering in sequential fusion.Source: §4.4 · pp. 7–8, Figure 6 · p. 8

5. Conclusion

The conclusion presents DANTE-AD as a dual-vision Transformer model for detailed, contextually rich long-form audio descriptions. It reports better performance than existing methods and identifies several directions for further work.

5. Conclusion

  • DANTE-AD sequentially fuses frame- and scene-level representations to model temporal context and ground fine-grained visual details in a coherent narrative.The conclusion contrasts this with prior methods relying only on frame-level embeddings.Source: §5 · p. 8
  • On real-world movie clips, DANTE-AD outperforms existing AD techniques and produces more emotive, story-rich results.The conclusion connects spatial and temporal modeling with bridging object-centric captioning and holistic storytelling.Source: §5 · p. 8
  • Future work may refine long-term scene understanding and adaptive attention, and add modalities such as audio and subtitles.Source: §5 · p. 8

6. LLM-AD-Eval Prompts

The paper uses the LLM-AD-Eval prompts from for LLaMA2-7B-chat and GPT-3.5-turbo. The prompts ask evaluators to judge predicted descriptions against references using visual elements and specificity.

6. LLM-AD-Eval Prompts

  • The authors reuse the LLM-AD-Eval prompts from for evaluations with LLaMA2-7B-chat and GPT-3.5-turbo.Source: §6 · p. 12, Figure 8 · p. 12, Figure 9 · p. 12
  • The prompt directs evaluators to assess coverage of visual elements, including actions, objects, and interactions, and to accept synonyms and paraphrases.Source: §6 · p. 12
  • Evaluators provide one score reflecting how well a prediction matches the reference, including whether it gives specific details tied to the video.Source: §6 · p. 12

7. Qualitative Examples

Figure 10 presents additional qualitative results for DANTE-AD on CMD-AD-Eval. The examples use sequential fusion cross-attention between frame- and scene-level visual embeddings.

7. Qualitative Examples

  • Figure 10 presents additional qualitative DANTE-AD examples.These results are on CMD-AD-Eval.Source: §7 · p. 12, Figure 10 · p. 13
  • The qualitative results are evaluated on CMD-AD-Eval.Source: Figure 10 · p. 13
  • The method uses sequential fusion cross-attention between frame- and scene-level visual embeddings.Source: Figure 10 · p. 13

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