Keyword-to-Caption Augmentation

• Keyword-to-caption augmentation is a multimodal approach that converts extracted keywords into full natural-language captions using methods like prompting, template expansion, and graph-based chaining.
• It leverages diverse extraction and decoding techniques—such as CLIP-keyword prompting and multitask decoding—to improve sample efficiency, with documented gains like +3 to +9 CIDEr on COCO and significant audio classification improvements.
• The methodology enhances data alignment and consistency by integrating explicit content control and augmentation strategies, ensuring semantic fidelity even in low-resource regimes and during transformations like image flipping.

Keyword-to-caption augmentation refers to a set of methodologies in multimodal learning where a system receives a discrete or structured set of “keywords”—typically concepts, events, attributes, or object classes extracted from input data—as intermediate representations and then generates full natural-language captions conditioned explicitly on these keywords. This paradigm is used to (1) improve sample efficiency in data-sparse regimes, (2) inject content control or increase factuality, (3) synchronize data augmentations between modalities, and (4) facilitate explainability by decomposing the language generation process. While originally motivated by the limitations of monolithic end-to-end captioning, keyword-to-caption augmentation now underpins diverse advances across image, audio, and video domains, both in supervised and low-/zero-shot settings.

1. Conceptual Foundations and Motivations

Keyword-to-caption augmentation emerges at the intersection of representation learning and prompt-based generation. Its essential motivation is to bridge the gap between high-level, often noisy or weak, semantic signals (e.g., image tags, object detections, audio event labels) and natural-language descriptions. This augmentation addresses several longstanding issues:

• Data alignment and scaling: Many large datasets (e.g., AudioSet, web image corpora) supply only keyword-level supervision. Generating natural-language captions from such supervision increases the volume and diversity of usable training data (Wu et al., 2022).
• Explicit content control and explainability: By conditioning on observable keywords, the model supports interpretable chains from semantics to language, crucial for auditing and for downstream applications with strict fidelity requirements (Birmingham et al., 2023).
• Handling data imbalance or low-resource regimes: Keyword-to-caption pipelines enable data-efficient training by leveraging pseudo-labels, compositionality, and mixing of human-annotated and automatically derived data (Li et al., 6 Nov 2024).
• Augmented data consistency: In tasks involving data augmentation that alters input semantics (such as image flipping), explicit keyword-to-caption updating preserves ground-truth consistency across modalities (Yi et al., 2023).

2. Methodological Variants

Keyword-to-caption augmentation admits diverse algorithmic frameworks, with differences along major axes: how keywords are extracted; how they are injected or used; and the style of language modeling employed.

Extraction and Selection

• Retrieval-based approaches: Use vision-LLMs (e.g., CLIP) to retrieve high-frequency or top-k relevant concepts based on input similarity in a shared embedding space (Cornia et al., 2021).
• Prediction-based approaches: Multitask models jointly estimate event or attribute keywords along with captions, leveraging the estimated set during autoregressive decoding (Koizumi et al., 2020).
• Rule- and classifier-based extraction: For synthetic pair generation, rules or scene graph parsers are used to extract unique object/relation/attribute combinations (Yao et al., 2022, Li et al., 6 Nov 2024).

Caption Generation and Integration

• Prompt-based decoding: Keywords are prepended or embedded as continuous/discrete prompts to transformer-based decoders, optionally with additional tokens specifying style or source (Cornia et al., 2021). In many frameworks, the prompt is fixed; the decoder generates the caption autoregressively, attending to keywords at every step.
• Template expansion: Keywords fill in slots in hand-crafted templates or prompt patterns, which can then be expanded by LLMs or smaller generators (Yao et al., 2022, Govindarajan et al., 16 Sep 2025).
• Graph-based chaining: N-gram graph methods search for the highest probability sentence covering all keywords, guided by n-gram statistics from large text corpora (Birmingham et al., 2023).
• Refined, guided sampling: Candidate captions are produced by manipulating pseudo-labels with edit actions, filtered for relevance to the original input via cross-modal encoders (e.g., X-CLIP-based similarity) (Li et al., 6 Nov 2024).
• Multi-modal decoding: Keyword features are fused with visual/audio features by gating or cross-attention in multi-stream transformer decoders (Li et al., 6 Nov 2024, Koizumi et al., 2020).

3. Detailed Architectural Examples

Image Captioning

• CLIP-keyword Prompting: Visual features ($V$) from a frozen CLIP-ViT backbone and BPE-tokenized keywords ($K$) are jointly used in a transformer decoder, with style tokens ($\theta$) controlling output fluency and content. The prefix $[K; \theta]$ explicitly conditions the model, and the cross-entropy loss is computed only on the autoregressive caption tokens. Quantitatively, this yields consistent CIDEr gains ($+3$ to $+9$) over ablations lacking keywords or style control, and enables long-tail name generalization (Cornia et al., 2021).
• N-gram Graph Decoding (KENGIC): A graph is constructed with keywords as nodes and n-gram collocates as edges/connecting labels. Caption inference is formulated as finding a high-probability path in the n-gram graph that covers all input keywords—requiring only a text corpus and a set of keywords, thus supporting zero or few annotations while remaining near SOTA for unpaired methods (Birmingham et al., 2023).
• Pseudo-label Augmentation with Lexical Constraints: In few-supervised video captioning, pseudo-labels are generated by editing extracted keyword sequences (using copy/replace/insert/delete operations), sampling plausible caption continuations, then filtering by X-CLIP similarity. A gated fusion module in the transformer decoder refines keyword alignment with visual content, validating the importance of content-relevant keyword propagation for supervision (Li et al., 6 Nov 2024).

Audio Captioning

• Contrastive Language–Audio Pretraining: Tag-only annotated audio clips (e.g., from AudioSet) are mapped into natural-language via T5, with template prompting such as, “Generate a descriptive audio caption for: car, engine, horn.” The resulting captions enter standard contrastive losses used for cross-modal retrieval, yielding large gains in retrieval metrics and zero-shot audio classification (notable: VGGSound ZS classification from 29.1% → 46.2% with K2C) (Wu et al., 2022).
• Multitask Keyword-Aided Decoders: TRACKE jointly minimizes keyword detection loss and caption-generation loss, concatenating estimated keyword embeddings with frame-wise features for the decoder, thus directly controlling ambiguity in auto-captioning by relating each generated token to salient input events (Koizumi et al., 2020).
• MAGIC-Enhanced Prompting and Decoding: Audio CLIP models provide a ranked keyword set via cross-modal similarity. The highest-confidence keywords are embedded into templated prompts (“This is a sound of dog bark and traffic”), which guide a downstream LLM to generate captions. During decoding, the MAGIC algorithm re-ranks candidate tokens at each step by a weighted combination of model confidence, degeneration penalty, and direct audio–text alignment, yielding 35% relative mean-score improvements on AudioCaps over non-keyword or non-MAGIC baselines (Govindarajan et al., 16 Sep 2025).

Grounded Data Augmentation

• Alignment-Preserving Augmentation: When image augmentations (e.g., horizontal flips) potentially invert semantic content (e.g., “left”/“right”), captions are automatically modified using an explicit affix- and prefix-preserving mapping over a pre-defined keyword set. This “flip-aware” keyword substitution delivers additional samples, increasing average precision by up to 6.7 points on referring-expression datasets and maintaining semantic consistency across views (Yi et al., 2023).

4. Training Objectives, Losses, and Learning Dynamics

Objective formulations vary by architecture:

• Prompted language modeling loss: Given (visual, keyword, style) prompts, the LLM is optimized with a standard token-wise log-likelihood; downstream SCST/CIDEr optimization may be added for performance tuning (Cornia et al., 2021).
• Multitask loss: When models learn both to extract keywords and generate captions, a weighted sum of binary cross-entropy (for keyword presence) and standard cross-entropy (for caption tokens) is minimized (Koizumi et al., 2020).
• Contrastive loss: Augmented keyword-to-caption pairs enter symmetric contrastive losses, e.g.,

$$L = - \frac{1}{2N} \sum_{i=1}^N \left[\log \frac{\exp(E_i^a \cdot E_i^t / \tau)}{\sum_{j=1}^N \exp(E_i^a \cdot E_j^t / \tau)} + \log \frac{\exp(E_i^t \cdot E_i^a / \tau)}{\sum_{j=1}^N \exp(E_i^t \cdot E_j^a / \tau)}\right]$$

(Wu et al., 2022).

• Classification and generation joint objective: In lexically constrained pseudo-labeling, a combination of XLNet action-classification loss, pseudo-label/caption cross-entropy, and refined keyword embedding similarity is used:

$$\mathcal{L} = \mathcal{L}_{\rm sen} + \mathcal{L}_{\rm word}$$

(Li et al., 6 Nov 2024).

Typically, ablation studies quantify the incremental utility of each component (e.g., keywords, style, web data) over strong baselines and existing SOTA results, often in terms of CIDEr, BLEU, METEOR, and CLIP-based ranking or retrieval metrics.

5. Empirical Impact and Evaluation

Augmentation via keyword-to-caption methods consistently improves downstream performance across metrics and settings:

• On COCO, keyword and style token prompting yields +9 CIDEr over a strong baseline, and enables generation of hundreds more long-tail semantic types (Cornia et al., 2021).
• For data augmentation in grounding, keyword-informed caption transformation increases AP by up to 6.7 points, outperforming both vanilla flipping and skip-on-keyword policies (Yi et al., 2023).
• In zero-shot and few-shot scenarios, keyword augmentation enables models to approach or exceed oracle performance—e.g., TRACKE’s keyword-aided decoding nearly matches systems given access to human tags (Koizumi et al., 2020), while PKG’s pseudo-labeling realizes large gains over prior few-shot video captioners (Li et al., 6 Nov 2024).
• In large-scale audio pretraining, converting tags to captions via T5 yields consistent, sometimes dramatic, gains not only in retrieval (+1.4%–1.5% R@1) but also in transfer-learning capacity, as seen in zero-shot recognition (+17% on VGGSound) (Wu et al., 2022).
• In LLM-guided zero-shot AAC, keyword prompting coupled with audio-conditioned decoding (MAGIC) delivers a 35% relative jump in NLG mean score, with ablations showing a >50% drop when keywords are absent (Govindarajan et al., 16 Sep 2025).

Typically, the greatest relative boosts are observed on out-of-domain, rare, or long-tail subsets, and for metrics sensitive to semantic richness or grounding (SPICE, CLIPScore, RefCLIPScore).

6. Limitations, Pitfalls, and Extensions

Major limitations and caveats identified across studies include:

• Reliance on keyword coverage: Missing or erroneous keywords in the input (due to detector or classifier errors) directly degrade caption relevance and fluency, especially in graph or template-based systems (Birmingham et al., 2023).
• Corpus and vocabulary dependence: Graph-based approaches need extensive text corpora for coverage; out-of-vocabulary concepts are poorly handled (Birmingham et al., 2023).
• Automated clause or detail extraction: Rule-based detail extraction can lead to noncanonical sentence structures; future work seeks insertion-based or learned compositional expansion (Yao et al., 2022).
• Prompt saturation and ambiguity: Increasing the number of keyword tokens passed to an LLM may introduce noise, interference, or increase decoding indeterminacy; performance is often optimal at one or two keywords per prompt (Govindarajan et al., 16 Sep 2025).
• Bias and demographic leakage: Automated caption generation from tag lists may retain or amplify biases present in the source data; explicit debiasing and heuristic replacement are sometimes applied (Wu et al., 2022).

Extensions and possible research directions include user-controllable prompts for style/detail, improved end-to-end integration of relation extraction, and adaptation of keyword-to-caption frameworks to video or cross-modal domains (medical, multilingual, etc.) (Govindarajan et al., 16 Sep 2025, Li et al., 6 Nov 2024).

7. Representative Approaches: Comparative Table

Below, recent keyword-to-caption augmentation architectures are summarized:

Approach / Paper	Extraction/Prompting	Decoder Integration	Domain
(Cornia et al., 2021)	CLIP kNN keyword retrieval	Transformer decoder, prefix prompting	Image
(Yi et al., 2023)	Regex positional substitution	Caption rewrite, consistency check	Image, grounding
(Yao et al., 2022)	Scene-graph, clause parsing	Prompt vector/template expansion	Image
(Koizumi et al., 2020)	Joint keyword estimation	Concatenation, cross-attention	Audio
(Birmingham et al., 2023)	POS/ML, external detector	N-gram graph path search	Image
(Wu et al., 2022)	Tag→caption (T5, template)	Pretrain text encoder (contrastive)	Audio
(Govindarajan et al., 16 Sep 2025)	CLIP embedding ranking	LLM prompt, audio-conditioned decoding	Audio (zero-shot)
(Li et al., 6 Nov 2024)	Token classifier, LM edits	Gated video–keyword fusion (Transformer)	Video

8. Conclusion

Keyword-to-caption augmentation has evolved into a central and unifying approach for controlled, reliable caption generation in vision, audio, and video. It leverages structured or automatically extracted intermediate representations for prompt-based or graph-based language modeling, consistently leading to improvements in natural-language output quality, data efficiency, and downstream multimodal understanding. Empirical results across recent literature demonstrate not only robustness to low-resource and heterogeneous settings, but also the instrumental role of explicitly conditioning on content-relevant keywords for explainability, transfer, and high compositional generalization. Ongoing research will likely focus on further integration with prompt-tuned generative LMs, enhanced multimodal alignment, and application-specific extensions such as clinical or cross-lingual captioning.