Papers by Wei-Lun Chao
Revisiting Document Representations for Large-Scale Zero-Shot Learning (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods for visual recognition use visual attributes carefully annotated by humans. |
| Approach: | They propose a semi-automatic mechanism for visual sentence extraction that leverages document section headers and clustering structure of visual sentences. |
| Outcome: | The proposed method improves on the ImageNet dataset with 10,000 unseen classes. |
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets (N18-1)
Copied to clipboard
| Challenge: | Visual question answering datasets are a form of (visual) Turing test that artificial intelligence should strive to achieve. |
| Approach: | They propose automatic procedures to remedy design deficiencies in visual question answering datasets . they propose to use a set of decoys to re-construct decoying answers for two popular Visual QA datasets. |
| Outcome: | The proposed procedures improve the performance of the proposed datasets. |
Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question Answering (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods address this issue by introducing an auxiliary task such as visual grounding, cycle consistency, or debiasing. |
| Approach: | They propose a data augmentation pipeline to turn “known” knowledge into training examples for VQA. |
| Outcome: | The proposed model can handle multi-modal information and is based on human-annotated examples. |