VQA-PDF: Purifying Debiased Features for Robust Visual Question Answering Task
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5615-5_22
Reference28 articles.
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2. Selvaraju, R.R., et al.: Taking a hint: leveraging explanations to make vision and language models more grounded. In: ICCV, pp. 2591–2600 (2019)
3. Wu, J., Mooney, R.: Self-critical reasoning for robust visual question answering. In: NeurIPS, vol. 32, pp. 8601–8611 (2019)
4. Chen, L., Yan, X., Xiao, J., Zhang, H., Pu, S., Zhuang, Y.: Counterfactual samples synthesizing for robust visual question answering. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10 800–10 809 (2020)
5. Gokhale, T., Banerjee, P., Baral, C., Yang, Y.: Mutant: a training paradigm for out-of-distribution generalization in visual question answering. In: EMNLP, pp. 878–89 (2020)
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