Multimodal Training Datasets

• Multimodal training datasets are curated corpora that align multiple data modalities such as text, images, audio, and video to support cross-modal learning.
• They involve systematic curation pipelines including web-scale data collection, filtering, and alignment using methods like CLIP-based similarity and graph-structured organization.
• Evaluation protocols focus on assessing inter-modality synergy and dependency structures to ensure robust performance across tasks like retrieval, captioning, and VQA.

Multimodal training datasets comprise corpora containing aligned or co-occurring observations from multiple data modalities (e.g., text, images, audio, video, sensor data). These datasets are foundational for research on models that must interpret, relate, or generate across modalities, and are pivotal in pre-training, supervised, and self-supervised paradigms for multimodal learning. Rigorous curation, characterization, and benchmarking of these datasets directly impact the fidelity, generalizability, and robustness of multimodal systems. Below, key dimensions underlying the construction, taxonomy, evaluation, and future trajectory of multimodal datasets are synthesized from primary literature and recent meta-analyses.

1. Taxonomy and Key Categories of Multimodal Datasets

The contemporary landscape is organized hierarchically along usage stage, task specificity, and domain alignment (Pattnayak et al., 2024):

• Pre-training Datasets (MM-PT): Massive web-scale corpora of image–text pairs (e.g., LAION-5B: 5.85B pairs, ALIGN: 1.8B pairs), or interleaved image–text streams, typically collected via web crawls and filtered by cross-modal similarity, toxicity, and deduplication. Video–text pairs (WebVid) and audio–text corpora (AudioSet, AISHELL-2) extend this paradigm to temporal and acoustic modalities.
• Instruction-Tuning Datasets (MM-IT): Curated sets in which multimodal content is cast into question–instruction–response structures. LLaVA-Instruct-150K and SVIT synthesize or annotate instructional pairs for visual QA, captioning, and compositional reasoning.
• Task-Specific Datasets: Benchmarks for defined applications such as VQA (e.g., SlideVQA, SQA3D), captioning (AudioCaps), retrieval (MSCOCO, InternVid), or emotion recognition (MELD), generally smaller in scale but annotated for fine-grained supervised evaluation.
• Domain-Specific Datasets: Focused on vertical applications (medical imaging—MIMIC-CXR, autonomous driving—nuScenes, satellite imagery—EuroSAT), often requiring specialized curation and annotation pipelines.

This taxonomy enables systematic tracking of dataset development along both modality coverage and application scope.

2. Construction Pipelines and Curation Methodology

Dataset construction spans multi-stage pipelines, typically comprising raw collection, rigorous filtering, and (for some benchmarks) explicit mutual information control or graph-based structuring.

Web-scale Corpus Construction

Large-scale image–text datasets such as LAION and DataComp CommonPool begin with web scraping via <img> tag parsing, extraction of nonempty alt-text, and aggressive download scaling (>10B raw pairs) (Gadre et al., 2023, Pattnayak et al., 2024). Filtering leverages:

• Language and caption length constraints
• Image format, size, and aspect ratio requirements
• Toxicity/NSFW filtering (Detoxify, CLIP-based image classifiers)
• Face blurring for privacy preservation
• Cross-modal similarity filtering (e.g., CLIP score, cosine similarity)—executed as

$f_{\rm clip}(x_i, y_i) = \cos(g(x_i), h(y_i))$

where $g$ and $h$ denote CLIP image/text encoders, and thresholding at $f_{\rm clip}\geq \tau$ optimizes alignment.

Curated subsets (e.g., DataComp-1B: 1.4B pairs) yield substantial accuracy gains over unfiltered pools, showing that data quality, not just scale, is critical (Gadre et al., 2023).

Synthetic and Controlled Generation

Frameworks for generating multimodal data with explicitly controllable mutual information (MI) leverage latent variable DAGs and flow-based generative models to produce high-dimensional samples where $I(X;Y)$ and $I(X_i;X_j)$ are analytically computable (often in closed form for Gaussian models), enabling benchmarking of MI estimators and controlled studies of SSL regimes (Hashmani et al., 24 Oct 2025).

Knowledge Graph Structured Multimodal Datasets

UKnow demonstrates the organization of multimodal data into large-scale knowledge graphs. Here, five partitioned relation types—in-image, in-text, cross-image, cross-text, and image–text grounding—encode structural relationships, facilitating both pre-training and logic-rich reasoning tasks (Gong et al., 2023).

Specialized Contexts and Dialogue

For applications involving dialog or context, pipelines combine text-centric dialogue sources and employ image substitution with contextual filtering, as in (Lee et al., 2021), where Visual Semantic Reasoning Networks (VSRN) score cosine similarity between dialogue turns and candidate images to generate multi-turn, multi-modal conversations.

3. Characterization of Dependency Structure and Benchmark Bias

A central axis in dataset design and evaluation is quantifying the degree to which benchmarks necessitate genuine multimodal reasoning as opposed to allowing unimodal shortcuts (Madaan et al., 27 Sep 2025):

• Intra-modality dependency: Measured by permutation tests isolating each modality (e.g., image-only, text-only) versus random alignment.
• Inter-modality synergy: Quantified as the additional gain only present for correctly paired multi-modal inputs:

$$\Delta_{\rm synergy} = A_{\rm N} - A_{\rm I} - A_{\rm T} + A_{\rm R}$$

where $A_{\rm N}, A_{\rm I}, A_{\rm T}, A_{\rm R}$ are task accuracies under normal, image-only, text-only, and random pairings, respectively.

Empirical studies show that only 5 of 23 contemporary VQA benchmarks exhibit “pure” synergy; most datasets are biased toward vision or text, and within-dataset category structure can mask shortcuts (e.g., question-only or image-only yielding high performance) (Madaan et al., 27 Sep 2025). These findings motivate design guidelines:

• Rigorous reporting of $$\Delta_{\rm I}, \Delta_{\rm T}, \Delta_{\rm synergy}$$ per benchmark
• In-distribution shuffling, adversarial balancing, and category-specific breakdowns for latent bias diagnosis

4. Preprocessing, Alignment, and Fusion Strategies

Dataset preprocessing and alignment are tailored to modality and application domain (Xue et al., 2020, Muhovič et al., 19 Dec 2025, Gadre et al., 2023):

• Images/videos: Scaling, cropping, color jitter, and face blurring; frame sampling and temporal cropping for video.
• Text: Tokenization (BERT, VLM-native), length normalization, minimal spelling/post-processing to preserve rawness in instructions or captions.
• Structured data/semantic labels: Wikidata entity traversal, triple-class representation, and geo-cell discretization for spatial grounding (Armitage et al., 2020).
• Alignment: Explicit caption–image or dialogue–image linking via text-image similarity metrics (e.g., CLIP/VSRN).
• Fusion: Early fusion (concatenation+FC), cross-modal attention (transformers with modality-specific queries), late fusion gating, or score-level linear combination (Xue et al., 2020, Wei et al., 2023, Gong et al., 2023).

Application-specific strategies infer robustness, e.g., double forward pass and modality heads in adverse-visibility segmentation, as in MULTIAQUA (Muhovič et al., 19 Dec 2025).

5. Evaluation Protocols, Benchmark Tasks, and Metrics

Multimodal dataset utility is ultimately measured via rigorous, often contrastive objectives applied across diverse downstream benchmarks:

• Contrastive Learning: InfoNCE loss, task-conditional instruction embedding, and in-batch negatives universally applied across retrieval, classification, and QA setups (Jiang et al., 2024, Gadre et al., 2023, Wei et al., 2023).
• Retrieval: Recall@k, mean/median rank, and coverage on large candidate pools (M-BEIR: 5.6M candidates), with instruction-conditioned queries and diverse source tasks (Wei et al., 2023).
• Captioning and QA: BLEU, METEOR, CIDEr, SPICE for caption generation; exact match and accuracy for QA (Pattnayak et al., 2024).
• Segmentation/Scene Understanding: Mean Intersection over Union (mIoU), class-wise metrics for pixel-level tasks (Muhovič et al., 19 Dec 2025).
• Mutual Information Analysis: Closed-form MI values provide baselines for estimators and enable SSL method comparison under known dependency regimes (Hashmani et al., 24 Oct 2025).

Unified, instruction-tuned benchmarks (e.g., M-BEIR, MMEB) cover both local (task-specific) and global (all-candidate) retrieval, enabling assessment of truly universal retriever models and cross-dataset generalization (Wei et al., 2023, Jiang et al., 2024).

6. Challenges, Limitations, and Future Directions

Despite transformative scale and scope, state-of-the-art multimodal datasets face open challenges (Pattnayak et al., 2024):

• Data Quality vs. Scale: Noise, weak alignment, and cultural bias in web-scale crawls; necessity for rigorous filtering, deduplication, and NSFW handling, as demonstrated in DataComp and LAION.
• Modality Imbalance: Underrepresentation of audio, video, 3D, and sensor data relative to image/text; emerging benchmarks expand but modality coverage remains unequal.
• Annotation Burden: High-quality benchmarks (medical, scientific, technical) require expert annotation, which cannot be matched by automated pipelines.
• Geographical and Linguistic Diversity: Datasets remain skewed toward English and certain regions; population-proportional subsampling methods (as in MLM) help mitigate, but coverage remains incomplete (Armitage et al., 2020).
• Cross-modal Alignment Complexity: Fine-grained alignment (e.g., region-level, temporal) remains a bottleneck; emerging graph-based protocols (UKnow) and transitive training (LoReTTa) offer architectural mitigations (Gong et al., 2023, Tran et al., 2023).
• Environmental Sustainability: Storage and compute costs of billion-scale corpora are significant; dataset distillation in the wild (MDW) demonstrates up to 30% efficiency improvements in retrieval at <0.1% data size (Dang et al., 2 Jun 2025).

Proposed solutions in the literature include expansion into underrepresented modalities (tactile, haptic, biological), institutionalized data documentation (“data cards”), benchmarking frameworks for cross-task/model comparability, and responsible curation pipelines balancing privacy, equity, and contamination avoidance (Pattnayak et al., 2024, Gadre et al., 2023).

7. Impact and Applications

The availability and quality of multimodal training datasets directly empower a wide spectrum of foundational and application-specific model capabilities:

• Pre-training universal multimodal models (“foundation models”) for transfer to a broad array of downstream tasks (Gadre et al., 2023, Jiang et al., 2024).
• Instruction-following, compositional reasoning, and goal-conditioned generation as enabled by instruction-tuning corpora and transitive alignment architectures (Tran et al., 2023, Wei et al., 2023).
• Benchmarked advances in VQA, text-to-image/video generation, grounding, retrieval, and cross-modal commonsense reasoning (Pattnayak et al., 2024, Gong et al., 2023).
• Domain-tailored deployments: e.g., robust perception for autonomous systems in challenging scenarios (MULTIAQUA), or fine-grained geo-localization and entity linking (MLM).

Adherence to principled dataset curation, annotation, and fine-grained analysis of modality dependency is identified as essential for the next generation of multimodal benchmarks, models, and applications.