Papers with VQA-CPv2
Causal Reasoning through Two Cognition Layers for Improving Generalization in Visual Question Answering (2023.emnlp-main)
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| Challenge: | Existing attempts to generalize VQA focus on unimodal aspects, overlooking enhancements in multimodal aspects. |
| Approach: | They propose to decompose the responsibility of each stage into distinct experts and a cognition-enabled component (CC) they prioritize answer predictions governed by pathways involving both CCs while disregarding answers produced by either CC. |
| Outcome: | The proposed model improves multimodal predictions by emphasizing causal reasoning factors. |
A negative case analysis of visual grounding methods for VQA (2020.acl-main)
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| Challenge: | Existing Visual Question Answering (VQA) methods exploit dataset biases and spurious statistical correlations instead of producing correct answers for the right reasons. |
| Approach: | They propose to incorporate visual cues to better ground VQA models . they also propose a regularization effect which prevents over-fitting to linguistic priors . |
| Outcome: | The proposed method outperforms existing methods on the Visual Question Answering (VQA) dataset. |