Research map / Vision-language and multimodal / Visual question answering
Image Content Generation with Causal Reasoning
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Proposes the VQAI task (causal image generation: an initial image plus a causal question yields an answer image) with a Tom and Jerry dataset of 17,524 samples and a latent-guided diffusion model (LGD) that conditions Stable Diffusion on frozen-LLM features via contrastive causal predictive coding and causal-chain supervision.
Problem
Image generation models follow explicit text guidance but fail to perform causal reasoning about implicit conditions, so they cannot generate the plausible visual outcome of a causal question given an initial image.
Approach
Guide the diffusion decoder with hidden-space features of a frozen LLM (T5-XXL via BLIP-2 Q-Former) rather than with a textual answer, using a space-translation layer, a contrastive causal predictive coding module, and causal-chain text supervision.
Key results
- LGD achieves CLIP Sim_Avg 0.8589 versus 0.8444 for AGD and 0.8361 for QGD on the VQAI test set.
- Human accuracy rises to 0.3239 for LGD versus 0.1852 (AGD) and 0.1695 (QGD); human chosen rate 0.5135 for LGD.
- Removing latent space translation lowers Sim_Avg from 0.8589 to 0.8414, the largest ablation drop.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use latent-guided generation instead of answer-guided generation | Flan-T5-XXL + Stable Diffusion on VQAI · VQAI test set | Sim_Avg (CLIP): 0.8444 -> 0.8589 | Helped |
| Use latent-guided generation instead of answer-guided generation | Flan-T5-XXL + Stable Diffusion on VQAI · VQAI test set | Sim_Best@9 (CLIP): 0.8867 -> 0.9038 | Helped |
| Use latent-guided generation instead of answer-guided generation | Flan-T5-XXL + Stable Diffusion on VQAI · VQAI test set | AUC_Avg (CLIP): 0.8394 -> 0.8539 | Helped |
| Use latent-guided generation instead of answer-guided generation | Flan-T5-XXL + Stable Diffusion on VQAI · VQAI test set | Acc (human): 0.1852 -> 0.3239 | Helped |
| Add latent space translation (LST) (ablation) | LGD · VQAI test set | Sim_Avg (CLIP): 0.8414 -> 0.8589 | Helped |
| Add contrast causal predictive coding (CCPC) (ablation) | LGD · VQAI test set | Sim_Avg (CLIP): 0.8482 -> 0.8589 | Helped |
| Add multimodal causal chain supervision (MCCS) (ablation) | LGD · VQAI test set | Sim_Avg (CLIP): 0.8487 -> 0.8589 | Helped |
| Add CCPC to guide features toward prediction space (ablation) | LGD · VQAI test set | Sim_Avg (CLIP): 0.8482 -> 0.8589 | No clear effect |
Surprising findings
- Question-guided editing with QGD yields zero human accuracy on scenery-variation samples, showing editors cannot infer implicit scene changes from premises alone.
- CLIP-based AUC_Best@9 barely changes when CCPC is removed, so the quantitative gain from CCPC is not obvious.
Limitations
- Dataset is built from a single cartoon series (Tom and Jerry), a simplified setting, and is highly imbalanced across the five sample categories.
- Evaluation relies on CLIP similarity to a single ground truth, which does not capture diversity; human evaluation uses only 10 annotators.
- Copyright of source videos; released annotations only under fair-use rationale.
Experimental setup
Datasets: VQAI (Tom and Jerry frames)
Benchmarks: VQAI
Metrics: Sim_Avg (CLIP) Sim_Best@9 (CLIP) AUC_Avg (CLIP) AUC_Best@9 (CLIP) human accuracy human chosen rate FID
Baselines: InstructPix2Pix (question-guided diffusion, QGD) answer-guided diffusion (AGD)
Builds on: Stable Diffusion InstructPix2Pix BLIP-2 CPC Flan-T5
Closest papers by meaning
- Thinking-while-Generating: Interleaving Textual Reasoning throughout Visual Generation (CVPR 2026 · similarity 0.88)
- Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation (ICLR 2024 · similarity 0.88)
- T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT (NeurIPS 2025 · similarity 0.88)
- ConVQG: Contrastive Visual Question Generation with Multimodal Guidance (AAAI 2024 · similarity 0.87)
- MMMG: A Massive, Multidisciplinary, Multi-Tier Generation Benchmark for Text-to-Image Reasoning (NeurIPS 2025 · similarity 0.87)
- Janus-Pro-R1: Advancing Collaborative Visual Comprehension and Generation via Reinforcement Learning (NeurIPS 2025 · similarity 0.87)
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