Research map / Vision-language and multimodal / Visual question answering
Answer Them All! Toward Universal Visual Question Answering Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add early fusion of visual region and question features (ablation) | RAMEN · VQAv2 | 10-choose-3: 61.81 -> 65.96 | Helped |
| Add early fusion of visual region and question features (ablation) | RAMEN · CLEVR | accuracy: 77.48 -> 96.92 | Helped |
| Add late fusion of bimodal embeddings with question features (ablation) | RAMEN · VQAv2 | 10-choose-3: 65.64 -> 65.96 | No clear effect |
| Add late fusion of bimodal embeddings with question features (ablation) | RAMEN · CLEVR | accuracy: 96.63 -> 96.92 | No clear effect |
| Use recurrent bi-GRU aggregation instead of mean pooling (ablation) | RAMEN · VQAv2 | 10-choose-3: 63.01 -> 65.96 | Helped |
| Use recurrent bi-GRU aggregation instead of mean pooling (ablation) | RAMEN · CLEVR | accuracy: 92.45 -> 96.92 | Helped |
Closest papers by meaning
- Answer-Type Prediction for Visual Question Answering (CVPR 2016 · similarity 0.94)
- Visual Question Answering as Reading Comprehension (CVPR 2019 · similarity 0.94)
- Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering (CVPR 2018 · similarity 0.93)
- Debiased Visual Question Answering from Feature and Sample Perspectives (NeurIPS 2021 · similarity 0.93)
- When Open-Vocabulary Visual Question Answering Meets Causal Adapter: Benchmark and Approach (AAAI 2025 · similarity 0.93)
- VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media Reasoning (CVPR 2024 · similarity 0.92)
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