Diverse Caption Generation

• Diverse caption generation is a field that develops methods to produce varied, human-like descriptions for the same input, moving beyond rigid, singular outputs.
• It leverages conditional GANs, VAEs, determinantal point processes, and control signals like POS guidance to systematically manage semantic and stylistic variation.
• Evaluation strategies include novel diversity metrics and human studies that ensure the generated captions are both informative and faithful to the content.

Diverse caption generation refers to the set of methodologies and frameworks designed to produce multiple, high‐quality natural language descriptions for the same visual or auditory input, each varying along dimensions such as semantics, syntax, pragmatic intent, and style. Unlike classical image, video, or audio captioning—where the focus is on generating a single, “average” or maximally likely caption—diverse caption generation aims to systematically explore the space of valid and human‐like expressions aligned with the multimodal content, thereby better reflecting the variability intrinsic to human annotation and description.

1. Motivations and Limitations of Conventional Captioning

Traditional captioning models, predominantly trained using maximum likelihood estimation (MLE), optimize n-gram overlap with ground truth references and rely heavily on metrics such as BLEU, METEOR, and CIDEr. This learning principle intrinsically penalizes valid but alternative phrasings, resulting in outputs that are “overly rigid and lacking in variability” (Dai et al., 2017). Such models tend to exhibit mode collapse: they favor high-frequency, template-like expressions and fail to capture the inherent ambiguity, richness, and diversity present in human descriptions (Chen et al., 2022).

A growing body of work demonstrates that this lack of diversity reduces interpretive power, limits information accessible to downstream tasks (retrieval, accessibility), and makes generated text less human-like—a shortcoming that has motivated diverse caption generation as an explicit research goal.

2. Conditional Generative Architectures for Diversity

A central approach to inducing diversity leverages stochastic, generative frameworks with explicit mechanisms for variation:

• Conditional GANs (CGANs): The framework in (Dai et al., 2017) utilizes a generator $G$ that synthesizes captions from an image feature vector $f(I)$ and an injected noise vector $z$, enabling $G$ to map the same image to multiple, varied captions. Adversarial training is implemented via an evaluator $E$ which distinguishes human and machine-generated captions, with the objective:

$$\min_{\theta} \max_{\eta} \mathbb{E}_{S\sim\mathcal{P}_I}[\log r_\eta(I,S)] + \mathbb{E}_{z\sim\mathcal{N}_0}[\log(1 - r_\eta(I, G_\theta(I,z)))]$$

Effective diversity is achieved by decoupling output variability from deterministic inputs and by employing Policy Gradient reinforcement learning with Monte Carlo rollouts to provide dense, early feedback signals in the word-generation process.

• Conditional VAEs: CVAEs, such as the AG-CVAE in (Wang et al., 2017), reparameterize a stochastic latent variable $z$ and structure its prior as dependent on detected image semantics. Notably, the Additive Gaussian prior blends multiple object-specific components: for detection vector $c \in \mathbb{R}^K$,

$$p(z|c) = \mathcal{N}(z | \sum_{k=1}^K c_k \mu_k, \sigma^2 I)$$

This allows sampling in the latent space to reflect rich, image-conditioned diversity, as opposed to collapsing to a generic mean.

• Reinforcing Determinantal Point Processes (DPP): R-DPP (Wang et al., 2019) introduces a DPP-based objective that maximizes the determinant of a Gram matrix encoding both caption quality and diversity. The DPP reward structure directly penalizes similarity among candidate captions while preserving high-likelihood modes, allowing models to generate diverse sets where individual modes correspond to high-quality, distinct captions.
• Comparative Adversarial Learning (CAL): The approach in (Li et al., 2018) proposes a comparative discriminator that operates on sets, computing a cr-score via softmax over the cosine similarity between candidate and reference image embeddings. This facilitates training a caption generator that is not only accurate but also produces outputs that are distinctive across images.

3. Diversity via Structure, Control, and Latent Guides

Recent methods in this field also deploy additional control signals, constraints, or higher-order structural guides to explicitly steer diversity:

• Part-of-Speech (POS) Guided Captioning: In (Deshpande et al., 2018), predicted POS tag sequences serve as “global summaries” or skeletons, and the decoder is conditioned on these to generate syntactically and structurally distinct captions. Quantization and clustering (e.g., k-medoids over 210K POS-sequence types) ensure manageable yet expressive diversity channels.
• Scene Graph and Abstract Graph Guidance: The ASG2Caption model (Chen et al., 2020) conditions caption generation on user- or system-specified Abstract Scene Graphs (ASGs), where object, attribute, and relationship nodes govern both what is described and the narrative structure.
• Diversity Regularization and Set-Level Objectives: SCG-SP (Lu et al., 2023) frames captioning as a set prediction problem where a diversity regularization loss is imposed on the concepts represented in each generated caption. The objective is to maximize the standard deviation across predicted concept probabilities within the set, thus encouraging semantically diverse coverage.
• Discrete Mode Embedding: DML (Chen et al., 2022) introduces a discrete codebook of “mode embeddings”; every caption is assigned a mode via a Hungarian-matched non-autoregressive variational autoencoder (CdVAE). Caption generation models (e.g., Transformers, AoANet) are modified to accept mode embeddings as auxiliary inputs, directly controlling syntactic and semantic diversity.
• Pragmatic Diversity via Coherence Relations: The RONA strategy (Ramakrishnan et al., 14 Mar 2025) uses prompt engineering on MLLMs to generate captions conforming to predefined coherence relations (Insertion, Concretization, Projection, Restatement, Extension), enabling pragmatic variation beyond surface language features.

4. Metrics, Evaluation Protocols, and Empirical Findings

Conventional automatic metrics (BLEU, METEOR, CIDEr) often insufficiently capture diversity, as n-gram overlap is inherently biased toward consensus patterns. As a result, new metrics have been proposed:

Metric	Assesses	Example/Definition
Self-CIDEr	Content diversity	Mean CIDEr similarity among generated captions
mBLEU (mutual BLEU)	Redundancy	BLEU between each pair of generated captions
Div-n	Lexical diversity	$$\operatorname{Div}-n = \frac{\text{\# unique }n\text{-grams}}{ \text{total }n\text{-grams}}$$
Coverage Ratio (CR)	OCR or concept coverage	Proportion of image tokens covered over caption set
Oracle/Consensus	Upper/retrieved metric	Max/aggregate over multiple outputs

Human preference studies remain central for evaluating qualitative gains. In (Dai et al., 2017), user studies found adversarially trained CGAN captions preferred over MLE-generated ones 61% of the time. In (Deshpande et al., 2018), POS-based captions outperformed beam search and VAE methods in pairwise human evaluations.

5. Cross-Modal and Document-Level Diversity

Recent work expands diverse caption generation to multimodal and document-specific settings:

• Audio Captioning via Adversarial Training: Both (Mei et al., 2021) and (Mei et al., 2022) extend C-GAN frameworks to audio modalities, with reward functions combining naturalness discriminators, semantic discriminators, and language metric evaluators, supported by reinforcement learning for discrete token selection. Empirical results on the Clotho dataset demonstrate increased corpus-level vocabulary and improved diversity metrics (lower mBLEU₄, higher div-n) over MLE models.
• Figure Captioning in Scientific Documents: The MLBCAP framework (Kim et al., 5 Jan 2025) integrates multimodal LLMs for quality filtering, diversified LLM-based candidate generation, and consensus/judgment via an expert LLM. Human evaluation ranks MLBCAP-generated captions above author-written ones in terms of informativeness and completeness, highlighting the benefit of modular, multi-expert architectures for contextually grounded diversity.

6. Applications and Theoretical Significance

The pursuit of diversity in caption generation is instrumental in multiple application domains:

• Image/video/audio retrieval: Semantically diverse caption sets provide more entry points for search and finer-grained indexing.
• Accessibility: More varied and nuanced descriptions support different user needs, especially for visually impaired users.
• Content creation: Rich, human-like caption variability enhances creative workflows in journalism, social media, and document summarization.
• Interactive and user-intention-driven captioning: Graph-guided and control-based methods (e.g., ASG2Caption, DML) enable real-time adaptation to user emphasis and style preference.

The theoretical underpinning is the alignment of computational caption generation with the human capacity for ambiguity, abstractness, and purpose-driven description, moving away from the “one-best” paradigm toward explicating the space of plausible explanations and narratives associated with data.

7. Open Questions and Research Directions

Despite considerable progress, central challenges persist:

• Balancing diversity and fidelity: Overemphasis on diversity can degrade the semantic alignment or factuality of captions, requiring sophisticated objective design (e.g., determinantal processes, concept regularization).
• Evaluation metrics: Most existing diversity measures are lexically inspired; however, assessing “meaningful” or “pragmatic” diversity remains an open problem.
• Controllability and interpretability: Fine-grained, user-driven control is nascent; frameworks such as RONA (Ramakrishnan et al., 14 Mar 2025) suggest pragmatic guidance via coherence relations is a tractable axis, but further granularity and application scope are being actively explored.
• Reusability and deployment: While many methods are modular or adaptable to varied decoder architectures, the interaction between large-scale pretrained systems and explicit diversity controls remains an open area, especially in the context of document-scale and multi-modal narrative construction.

In summary, diverse caption generation is a rapidly evolving field defined by advances in probabilistic modeling, adversarial learning, structured control, and cross-modal expansion. The unifying principle is to move captioning systems beyond genericity, ensuring outputs are not only accurate with respect to their input but also varied, informative, and well-aligned with the diversity inherent in natural communication.