Papers by Timothy Ossowski
Retrieving Multimodal Prompts for Generative Visual Question Answering (2023.findings-acl)
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| Challenge: | Visual question answering (VQA) is a multimodal machine learning problem that challenges a model to answer a question posed about an image. |
| Approach: | They propose a generative model enhanced by multimodal prompt retrieval that integrates retrieved prompts and multimodal features to generate answers in free text. |
| Outcome: | The proposed model outperforms its non-retrieval counterpart by 30% on medical VQA tasks. |
How does Multi-Task Training Affect Transformer In-Context Capabilities? Investigations with Function Classes (2024.naacl-short)
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| Challenge: | Multi-task learning (MTL) for generalist models is a promising direction that offers transfer learning potential. |
| Approach: | They propose to combine multi-task learning (MTL) with in-context learning (ICL) to build models that can generalize to multiple tasks while being robust to out-of-distribution examples. |
| Outcome: | The proposed training strategies enable models to learn difficult tasks while mixing in prior tasks, denoted as mixed curriculum. |
OLIVE: Object Level In-Context Visual Embeddings (2024.acl-long)
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| Challenge: | Existing vision-language models lack fine-grained object-level understanding and grounding . existing models implicitly align text tokens with image patch tokens, which is ineffective for embedding alignment at the same granularity and introduces noisy spurious background features. |
| Approach: | They propose a method to prompt large language models with in-context visual object vectors . this method allows for controllable object-level reasoning . |
| Outcome: | The proposed method achieves competitive referring object classification and captioning performance while offering zero-shot generalization and robustness to visually challenging contexts. |
Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment (2022.findings-emnlp)
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Tuan Dinh, Jy-yong Sohn, Shashank Rajput, Timothy Ossowski, Yifei Ming, Junjie Hu, Dimitris Papailiopoulos, Kangwook Lee
| Challenge: | Recent studies show that unsupervised word translation is more accurate and robust without parallel corpora. |
| Approach: | They propose a method for unsupervised word translation that leverages visual observations and pretrained language-image models to align words. |
| Outcome: | The proposed method improves on the state-of-the-art language-image pretraining method for bilingual word alignment. |