Cartoon-Based VQA Datasets

• Cartoon-based VQA datasets are benchmarks designed to assess visual-linguistic reasoning using stylized imagery with exaggerated features and abstract contexts.
• They employ rigorous methodologies including exhaustive frame sampling, multi-stage quality control, and expert annotation to generate diverse QA pairs.
• These datasets expose model weaknesses in causal inference and spatial reasoning under non-photorealistic conditions, guiding future domain adaptation strategies.

Cartoon-based Visual Question Answering (VQA) datasets are specialized benchmarks designed to assess and advance multimodal models’ capacity for visual-linguistic reasoning over stylized, non-photorealistic imagery. Unlike conventional VQA datasets relying on natural scenes, cartoon-based resources leverage the distinctive properties of animation—exaggerated characters, simplified textures, narrative-driven context, and unambiguous cause–effect relationships—to expose fundamental challenges in model transfer, compositional generalization, and causal inference.

1. Motivation and Domain Distinction

Cartoon-based VQA arises from the observation that vision–LLMs fine-tuned on real-image benchmarks (e.g., VQA v2.0, GQA) exhibit significant degradation when confronted with stylized imagery. Photorealistic datasets bias models toward natural textures, lighting cues, and object appearances; by contrast, cartoons introduce iconic characters, synthetic color palettes, sharp outlines, and deliberately abstract backgrounds. These domain deviations surface persistent model weaknesses, including misclassification of boundary-confined objects, spatial reasoning failures due to non-gradient edges, and brittleness in attribute recognition under non-naturalistic color conventions (Huynh et al., 2024).

Moreover, cartoons serve as an ideal medium for inquiry-based learning, especially in early childhood education and cognitive impairment contexts. Their reduced visual complexity and salient cultural references (e.g., episodes from "The Simpsons" or "Tom & Jerry") enable accessible, interactive exploration without overwhelming the cognitive load, bridging the gap between real-world complexity and pedagogically-effective abstraction (Huynh et al., 2024).

2. Dataset Construction Methodologies

The construction and annotation pipelines vary by dataset but share common principles: exhaustive frame sampling, rigorous content filtering, systematic annotation, and multi-stage quality control.

SimpsonsVQA

• Frame extraction: 220 "Simpsons" episodes (seasons 24–33), one frame every 5 seconds. After filtering for inappropriate or trivial scenes and deduplication (kNN on deep features, $k=3$), 23,269 unique images remain.
• QA generation: Multi-sentence captions produced by OFA (fine-tuned on localized narrative datasets) serve as prompts for LLM-based (ChatGPT) QA pair generation, yielding 166,533 diverse, humanlike pairs. Trivial or repetitive questions are systematically removed.
• Annotation: Each (image, question, answer) triple is independently adjudicated by three high-performing Amazon Mechanical Turk annotators for relevance and answer correctness (“correct”, “incorrect”, “ambiguous”). Annotation volume is approximately 500,000, ensuring high inter-annotator agreement rates (Huynh et al., 2024).

VQAI (Tom & Jerry)

• Source: 755 episodes, automatic scene segmentation (~35 segments/episode), manual frame pairing within segments to capture causal transitions (Init → Answer frame), 17,524 image pairs.
• Annotation protocol: Each pair receives a causally-conditional question (“What happens if …?”), strictly assigned to one of five fine-grained causal event taxonomies (scenery, entity variation, etc.). Expert-reviewed annotation pipeline enforces semantic rigor and template compliance (Li et al., 2023).

CausalChaos! (Tom & Jerry)

• Materials: 161 cartoon episodes, 4,945 segment clips, each annotated with a causal “Why” question, single best answer, and detailed causal explanation, supporting multi-level answer formats and hard-negative distractor sampling via both embedding and LLM-based causal confusion synthesis (Parmar et al., 2024).

Pororo VQA

• Based on "Pororo the Little Penguin" short animated GIFs and static frames, with questions derived from subtitles. Conversion from original five-way multiple choice to open-ended answer format aligns the set with modern open-domain VQA evaluation (Wu et al., 6 Jan 2026).

3. Dataset Structure and Annotation Statistics

A summary of key dataset statistics is shown below:

Dataset	Images / Clips	QA Pairs	Annotation Scope / Types
SimpsonsVQA	23,269	166,533	Free-form, open-ended; 500k+ judgments; topic diversity: attribute (38%), object (29%), counting (12%), spatial (10%), action (9%) (Huynh et al., 2024)
VQAI (Tom&Jerry)	17,524 pairs	17,524	Causal frame pairs; per-pair cause-effect question; 5 event types (Li et al., 2023)
CausalChaos!	4,945 clips	4,945	“Why” QA+Explanation All; 4 hard distractors (MCQA); multi-level (Parmar et al., 2024)
Pororo VQA	Not specified	N/A	Frame-based, open-ended QA, narrative-centric (Wu et al., 6 Jan 2026)

In SimpsonsVQA, question forms are distributed as “What” (55%), “Is/Are/Can/Do/Does” (20%), “How many” (12%), and others (13%). Corresponding answer types are yes/no (27%), numeric (12%), and open-ended (61%). This compositional diversity targets broad reasoning classes. In CausalChaos!, the mean causal-chain length is approximately 2.7 (cf. 1.0 in NextQA, CausalVidQA); average four scene changes per clip, supporting dynamic, multi-step causal inference (Parmar et al., 2024).

4. Task Definitions and Evaluation Protocols

Cartoon-based VQA datasets support a range of visual-linguistic tasks:

1. Conventional VQA: Given $(i, q)$, predict answer $a$; formalized as $$f_\mathrm{VQA}:(\mathcal{I},\mathcal{Q})\rightarrow\mathcal{A}$$.
2. Irrelevant Question Detection: Binary classification, $$f_\mathrm{rel}:(\mathcal{I},\mathcal{Q})\rightarrow\{0,1\}$$.
3. Answer Correctness (Reverse Evaluation): Given $(i, q, a)$, predict evaluation label as $$f_\mathrm{eval}:(\mathcal{I},\mathcal{Q},\mathcal{A})\rightarrow\{\mathrm{correct},\,\mathrm{incorrect},\,\mathrm{ambiguous}\}$$ (Huynh et al., 2024).

For video/causal datasets:

• Causal Why-QA: Models must generate primary answers and explanations for “Why” questions, often over segments with multiple event transitions and require explicit chaining of causal steps (Parmar et al., 2024).
• Content Generation (VQAI): Predict future image $I_1$ conditioned on initial $I_0$ and text question $q$, incorporating causal reasoning and generative modeling (Li et al., 2023).

Evaluation protocols incorporate accuracy (single/multi-choice), F1 for class-imbalanced tasks, as well as multimodal generation metrics (CLIP similarity, FID, Caps-MIX, and human causal correctness vote rate). For open-ended questions, LLM-based semantic equivalence scoring (five-level scale) and standard text metrics (BLEU, ROUGE, METEOR, BERTScore, BLEURT) are applied (Wu et al., 6 Jan 2026, Li et al., 2023).

5. Baseline Performance and Model Analysis

Benchmarking results consistently indicate that SOTA vision–LLMs, including large-scale zero-shot and few-shot architectures (e.g., ChatGPT-4o, OFA, X-VLM, Mutan+Att), underperform substantive baselines established on real-image VQA datasets when evaluated on cartoon-based VQA (Huynh et al., 2024). Fine-tuned OFA achieves 82% test accuracy on SimpsonsVQA’s correct-labeled subset, while zero-shot GPT-4o trails at 68.32%. For question relevance and correctness, traditional models maintain moderate accuracy, but performance degrades on ambiguous or open-ended categories (e.g., F1 < 0.20).

In CausalChaos!, specialized spatiotemporal architectures (MIST) reach ≈60.5% multiple-choice QA accuracy (down to 40.6% with causally-confusing negatives), and only ≈ 43.7% on joint answer-and-explanation. CapsMIX scores for open-ended explanation generation remain <10%, underscoring the challenge of integrating visual context with multi-step causal logic (Parmar et al., 2024).

Multi-agent transformer systems incorporating visual, language, and critic agents (as in (Wu et al., 6 Jan 2026)) show incremental gains over single-agent baselines—e.g., SimpsonsVQA: 0.8403 (Language Only) to 0.8819 (Full system)—with agent-level ablations revealing that “visual agents” grounded in cartoon-specific encoding confer the largest boost, particularly for visually explicit or color/identity-driven queries.

6. Unique Challenges and Limitations

Cartoon-based VQA datasets expose weaknesses not apparent in natural-image benchmarks:

• Stylization effects: Character outlines, exaggerated proportions, and abstract backgrounds disrupt priors and region detector reliability.
• Narrative and temporal abstraction: Reasoning often demands multi-frame memory, reference to off-screen contexts, and inference of implied causal relationships, which standard models (reliant on frame-local context) cannot manage effectively (Parmar et al., 2024, Li et al., 2023).
• Domain shift: LLM-generated questions may fail to reflect human natural inquiry, and ambiguity often arises from lack of fine-grained context in visual or textual inputs (Huynh et al., 2024).
• Evaluation bottlenecks: Open-ended generation and multi-level explanation remain unsolved, with automated and human metrics lagging behind intuitive baselines.

7. Comparative Analysis and Future Directions

Relative to real-image VQA (VQA v2.0, GQA), where fine-tuned models yield >90% accuracy on binary queries, and synthetic abstract datasets (CLEVR, SHAPE) focused on compositional logic, cartoon-based resources introduce the largest representational gap due to stylization/narrative demands and extensive causal chain modeling (Huynh et al., 2024). They uniquely support multi-level answers, require explicit actor roles (named characters), and foreground dynamic scene linking.

Suggested next steps include:

• Domain adaptation and style transfer: Augment pretraining and fine-tuning with synthetic cartoon data, style-transferred images, and cartoon-specific backbone models.
• Explicit causal-graph modeling: Develop architectures capturing extended cause–effect sequences across visual, spatial, and temporal dimensions.
• Multi-turn, dialogic QA: Model multi-step, tutoring-style dialog with error type incorporation (e.g., common miscounts in early learners).
• Cross-domain robustness: Integrate diverse cartoon sources (e.g., anime, Western comics) to achieve generalization and style-invariant reasoning (Huynh et al., 2024, Li et al., 2023, Parmar et al., 2024).

Cartoon-based VQA datasets function as critical diagnostics for vision–language understanding, representing a rigorous testbed for generalization, causal scene understanding, and robust inquiry-based interaction across stylized visual domains.