Structured Caption Frameworks

• Structured Caption Frameworks are defined by decomposing text into explicit, semantically-rich fields, ensuring parseability and controlled generation.
• They employ specialized encoders and unified multimodal language models to decouple interpretation from synthesis, supporting diverse applications.
• These frameworks improve downstream tasks like video synthesis and document QA while addressing challenges in scalability, alignment, and detailed output.

Structured caption frameworks formalize the process of generating machine-interpretable, semantically-rich textual descriptions for images, videos, and multimodal content. Distinguished from generic captioning, these frameworks impose explicit field or slot-based schema on captions, facilitating alignment, controllability, and compositional reasoning in downstream vision-language and generative models. Recent advancements leverage large-scale multimodal LLMs (MLLMs), instruction-tuned datasets, and specialized encoders for robust coverage of visual (and non-visual) conditions. Such frameworks play a foundational role in controllable generation, human-aligned accessibility, and scientific document understanding.

1. Taxonomy of Structured Caption Frameworks

Structured caption frameworks are characterized by their explicit decomposition of captions into multiple, semantically-defined fields. Notable schema include:

• Six-component schemes: Dense scene, main object, background, camera, style, action, as exemplified in Any2Caption for video generation (Wu et al., 31 Mar 2025).
• Instance-based slotting: Per-instance structured templates with fields for class, appearance, action & motion, and position, as in InstanceCap (Fan et al., 12 Dec 2024).
• Four-part image caption templates: Subject, setting, aesthetics, camera, exemplified in Re-LAION-Caption 19M for text-to-image (Merchant et al., 7 Jul 2025).
• Information-theoretic pyramids: Local–global integration of semantic units, as in PoCa (Chen et al., 1 May 2024).
• Domain/domain-specific structural tokens: Table rows/columns, axis legends, structured text for scientific diagrams and documents (Lu et al., 9 Apr 2025, Kim et al., 5 Jan 2025).

The central design principle is to ensure parseability by downstream models and consistency across data instances, which enhances model alignment and utility.

2. Model Architectures and Encoding Strategies

Structured caption pipelines typically consist of:

• Specialized Encoders: Modal-specific modules for text, image, video, pose, depth, camera trajectory, and sometimes OCR-based text (Wu et al., 31 Mar 2025, Lu et al., 9 Apr 2025).
• Unified Multimodal LLMs: Pretrained MLLMs (e.g., Qwen2, GPT-4o) serve as centralized decoders, consuming encoded representations augmented with domain- or field-specific tokens (<|row_i|>, <|axis_x|>, <|motion_start|>, etc.) (Wu et al., 31 Mar 2025, Lu et al., 9 Apr 2025).
• Condition–Caption Decoupling: Interpretation (multimodal encoding, fusion, and slot filling) is decoupled from synthesis/generation, enabling use of off-the-shelf generative backbones without their retraining (Wu et al., 31 Mar 2025).
• Auxiliary Models: Object detectors, segmenters (e.g., SAM2), motion heads, and positive/negative lexica are used for instance extraction and semantic guiding (Fan et al., 12 Dec 2024).

This modular architecture permits scalable extension to new modalities and robust integration of diverse user and environmental conditions.

3. Training Objectives, Datasets, and Evaluation

Structured captioning frameworks employ tailored datasets and loss functions:

• Cross-Entropy over Structured Slots: Token-level likelihoods for multi-field outputs; e.g., loss functions of the form

$$L = -\sum_{(T,C,S^*)\in D} \sum_{t=1}^{|S^*|} \log P(s^*_t | s^*_{<t}, T, C)$$

(Wu et al., 31 Mar 2025).

• Auxiliary Objectives:
  • Fidelity losses (e.g., 3DVAE latent distance for video-caption alignment) (Fan et al., 12 Dec 2024).
  • Structure consistency loss for table/markdown captions (Lu et al., 9 Apr 2025).
  • Information-theoretic trade-offs for sufficiency, redundancy, and interpretability (Chen et al., 1 May 2024).
• Curated Datasets:
  • Any2CapIns: 337K video-caption pairs, 407K multimodal condition annotations (Wu et al., 31 Mar 2025).
  • InstanceVid: 22K videos, structured per-instance field annotations (Fan et al., 12 Dec 2024).
  • Re-LAION-Caption 19M: 19 million 1024×1024 images with four-part structured captions (Merchant et al., 7 Jul 2025).
  • MLBCAP: Scientific figure-caption triplet filtering and ensemble labeling (Kim et al., 5 Jan 2025).

Metric suites include standard generation measures (BLEU, ROUGE-L, METEOR, BERTScore), task-oriented alignment (CLIP similarity, VQA-based alignment), structure integrity (field presence), and human preference ranking.

Representative Evaluation Results

Model/Framework	Structured Caption Integrity	Downstream Alignment Gains
Any2Caption (video)	91.25% (six fields present)	CLIP-T, smoothness boost, lower RotErr (Wu et al., 31 Mar 2025)
InstanceCap (video)	Highest detail, lowest hallucination	Best 3DVAE, +5–10% detail/action on Inseval (Fan et al., 12 Dec 2024)
Re-LAION (images)	19,038,079 captions, rigid 4-field	+0.5–1.1 VQA points in image-text alignment (Merchant et al., 7 Jul 2025)
OmniCaptioner	Table/DocQA up to +15–25% absolute	Text-to-image GenEval +2–3 points (Lu et al., 9 Apr 2025)

4. Applications Across Modalities

Structured caption frameworks have broad applicability:

• Controllable Video Generation: Any2Caption enables “any-condition” synthesis controlled by text, images, pose, and motion; InstanceCap extends to per-instance fidelity (Wu et al., 31 Mar 2025, Fan et al., 12 Dec 2024).
• Text-to-Image Synthesis: Rigid slot-based templates yield improved adherence to user prompts and compositional accuracy (Re-LAION-Caption 19M) (Merchant et al., 7 Jul 2025).
• Dense Image/Region Captioning: Region-specific, length-conditioned generation (FlexCap); high AP on Visual Genome (Dwibedi et al., 18 Mar 2024).
• Document and Table QA: Structured captions for diagrams/tables facilitate downstream reasoning and question-answering with LLMs (OmniCaptioner, MLBCAP) (Lu et al., 9 Apr 2025, Kim et al., 5 Jan 2025).
• Accessible Non-Speech Captioning: CapTune enables DHH viewers to select expressiveness and detail via anchored semantic sliders, bounded by creator intent (Huang et al., 27 Aug 2025).
• Interactive/Multimodal Utilities: Caption Anything (CAT) unifies visual/language controls for custom region description and style transfer with foundation models (Wang et al., 2023).

Frameworks are thus foundational in enabling robust interpretability and fine-grained control for both machine and human consumers.

5. Best Practices, Limitations, and Generalization

Emergent best practices identified across recent frameworks include:

• Decoupling Condition Interpretation from Generation: Treating mature generative backbones as fixed and focusing resources on sophisticated multimodal interpretation yields high controllability with minimal retraining (Wu et al., 31 Mar 2025).
• Structured, Field-Based Grammars: Explicit, parseable multi-slot schemas ensure completeness and facilitate robust output/condition alignment (Wu et al., 31 Mar 2025, Fan et al., 12 Dec 2024, Merchant et al., 7 Jul 2025).
• Alignment-then-Tuning Paradigm: Aligning encoders with a frozen MLLM prior to instruction fine-tuning optimizes token space coverage (Wu et al., 31 Mar 2025).
• Sentence/Field Dropout During Training: Simulating concise user prompts improves robustness and supports real-world brevity (Wu et al., 31 Mar 2025).
• Instance-Aware Processing: Direct instance extraction and slotting reduce hallucination, especially in complex scenes (Fan et al., 12 Dec 2024).
• Inter-model Ensembles and Post-Selection: Multi-LLM collaborative appraisal and candidate selection outperform monolithic captioners, especially in scientific domains (Kim et al., 5 Jan 2025).

Documented Limitations

• Dependence on pre-trained detector quality and slot coverage: rare classes and subtle events may be underrepresented (Fan et al., 12 Dec 2024, Wu et al., 31 Mar 2025).
• Annotation and inference efficiency: multi-stage pipelines and complex merges introduce computational overhead (Fan et al., 12 Dec 2024, Chen et al., 1 May 2024).
• Data scale for rare modalities: expanding labeled datasets (InstanceVid, Any2CapIns) remains necessary for broader generalization (Fan et al., 12 Dec 2024, Wu et al., 31 Mar 2025).

A scalable future approach is to introduce new encoders and matching structured slots as new sensory modalities (audio, haptics, 3D shapes) become relevant, always maintaining the decoupled, slot-based interpretability.

6. Broader Context and Future Directions

Structured caption frameworks are foundational for the next generation of vision-LLMs with enhanced controllability, interpretability, and fidelity. Their modular, data-centric architectures are amenable to further advances:

• Extension to tri-modal and multi-modal settings (vision, language, audio, etc.)
• Integration with large-scale LLMs for flexible, reasoning-augmented applications (multi-hop reasoning on diagrams and scientific figures) (Lu et al., 9 Apr 2025, Kim et al., 5 Jan 2025)
• Dynamic user- or task-adaptive slot selection, as demonstrated in accessibility-centric frameworks such as CapTune (Huang et al., 27 Aug 2025)
• Framework generalization to domain-specific structured documents, code, and complex scientific illustration.

These directions underscore the critical importance of explicit structure, modular fusion, and large-scale, well-curated instruction data in advancing vision-language alignment and controllable generative modeling.