Papers by Wenjian Ding
Exploring Union and Intersection of Visual Regions for Generating Questions, Answers, and Distractors (2024.emnlp-main)
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| Challenge: | Existing efforts to generate image-related questions, correct answers, or challenge distractors are limited. |
| Approach: | They propose to put the spotlight on different image regions to diversify QADs . they propose a framework that generates each QAD based on a recurrent multimodal encoder . |
| Outcome: | The proposed framework puts the spotlight on different image regions to diversify QADs. |
Generating Questions, Answers, and Distractors for Videos: Exploring Semantic Uncertainty of Object Motions (2025.findings-acl)
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| Challenge: | Existing video QADs are generated using video captions, incurring significant costs and resulting in bias. |
| Approach: | They propose to use temporal motion to describe video objects to generate diverse QADs focusing on different objects and interactions. |
| Outcome: | The proposed approach improves consistency and diversity of generated QADs on the NExT-QA and Perception Test benchmarks. |
Can We Learn Question, Answer, and Distractors All from an Image? A New Task for Multiple-choice Visual Question Answering (2024.lrec-main)
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| Challenge: | Existing studies focus on generating QADs from image and question, but a novel task is needed to generate meaningful questions, correct answers, and challenging distractors. |
| Approach: | They propose a task to generate QADs from images and encode images together . they use contrastive learning to ensure consistency of QAD generated and tested . |
| Outcome: | Empirical evaluations on the benchmark dataset validate the performance of the proposed task. |