Papers by Robik Shrestha
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. |
BloomVQA: Assessing Hierarchical Multi-modal Comprehension (2024.findings-acl)
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Yunye Gong, Robik Shrestha, Jared Claypoole, Michael Cogswell, Arijit Ray, Christopher Kanan, Ajay Divakaran
| Challenge: | Recent advances of machine intelligence solutions have demonstrated tremendous success in a wide range of language and multi-modal tasks over diverse domains. |
| Approach: | They propose a VQA dataset to facilitate comprehensive evaluation of large vision-language models on comprehension tasks. |
| Outcome: | The proposed dataset shows improved accuracy over all comprehension levels and a tendency to bypass visual inputs especially for higher-level tasks. |