Papers by Soravit Changpinyo
MaXM: Towards Multilingual Visual Question Answering (2023.findings-emnlp)
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Soravit Changpinyo, Linting Xue, Michal Yarom, Ashish Thapliyal, Idan Szpektor, Julien Amelot, Xi Chen, Radu Soricut
| Challenge: | Visual Question Answering (VQA) has been studied in the English language, but in other languages it would require a considerable amount of resources. |
| Approach: | They propose scalable solutions to multilingual visual question answering using an English language framework and an annotation protocol. |
| Outcome: | The proposed framework reduces human annotation efforts and creates a test-only VQA benchmark in 7 languages. |
Multi-Task Learning for Sequence Tagging: An Empirical Study (C18-1)
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| Challenge: | Existing work on "pairwise" MTL has been validated in sequence tagging but key issues remain about its effectiveness. |
| Approach: | They propose three general multi-task learning approaches on 11 sequence tagging tasks. |
| Outcome: | The proposed approaches improve on 11 sequence tagging tasks. |
All You May Need for VQA are Image Captions (2022.naacl-main)
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| Challenge: | Visual Question Answering (VQA) has benefited from increasingly sophisticated models, but has not enjoyed the same level of engagement in terms of data creation. |
| Approach: | They propose a method that automatically derives VQA examples at volume by leveraging existing image-caption annotations combined with neural models for textual question generation. |
| Outcome: | The proposed method improves state-of-the-art zero-shot accuracy by double digits and achieves robustness that lacks in the same model trained on human-annotated VQA data. |
Denoising Large-Scale Image Captioning from Alt-text Data Using Content Selection Models (2022.coling-1)
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| Challenge: | Recent approaches to training large-scale image captioning (IC) models often fall short in terms of performance in leveraging noisy datasets in favor of clean annotations. |
| Approach: | They propose a technique that breaks down the task into two smaller, more controllable tasks - skeleton prediction and skelet-based caption generation. |
| Outcome: | The proposed method can generate better and denoised captions when using noisy datasets. |
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions? (2023.emnlp-main)
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| Challenge: | Pre-trained vision and language models have demonstrated state-of-the-art capabilities over existing tasks involving images and texts. |
| Approach: | They analyze a visual question answering dataset tailored for info-seeking questions . they show that pre-trained visual and language models can use fine-grained knowledge . |
| Outcome: | The proposed dataset elicits models to use fine-grained knowledge learned during pre-training. |
CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization (2021.emnlp-main)
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| Challenge: | Existing visual question answering models leverage spurious biases and take shortcuts to improve performance. |
| Approach: | They propose a semi-automatic framework for generating disentangled shifts by introducing a controllable visual question-answer generation module that generates highly-relevant question-announcer pairs with the desired dataset style. |
| Outcome: | The proposed framework generates highly-relevant and diverse question-answer pairs with the desired dataset style. |
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering (D19-1)
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| Challenge: | Object detection is used in vision and language tasks but is expensive to learn . popular models rely on annotating ground-truths for bounding boxes and semantic labels . empirically, object detection leads to effective transfer learning and improved captioning and visual question answering models . |
| Approach: | They examine the effect of decoupling box proposal and featurization on down-stream tasks . they propose a family of "two-stage" object detectors that propose category-agnostic bounding boxes . |
| Outcome: | The proposed method improves image captioning and visual question answering models by leveraging large amounts of labeled annotations. |