Papers by Christoph Feichtenhofer
VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding (2021.findings-acl)
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Hu Xu, Gargi Ghosh, Po-Yao Huang, Prahal Arora, Masoumeh Aminzadeh, Christoph Feichtenhofer, Florian Metze, Luke Zettlemoyer
| Challenge: | Existing methods for multimodal video understanding are task-specific, limiting their use for retrieval-style end tasks. |
| Approach: | They propose a task-agnostic multimodal pre-training approach that can accept video or text input, or both, for a variety of end tasks. |
| Outcome: | The proposed approach outperforms existing methods on a wider range of tasks while maintaining separability. |
Altogether: Image Captioning via Re-aligning Alt-text (2024.emnlp-main)
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Hu Xu, Po-Yao Huang, Xiaoqing Tan, Ching-Feng Yeh, Jacob Kahn, Christine Jou, Gargi Ghosh, Omer Levy, Luke Zettlemoyer, Wen-tau Yih, Shang-Wen Li, Saining Xie, Christoph Feichtenhofer
| Challenge: | Existing captioning models ignore existing alt-text metadata and lack transparency if training data is unknown. |
| Approach: | They propose an approach to edit and re-align alt-texts associated with images using human annotation. |
| Outcome: | The proposed approach improves image captions and improves text-to-image generation and zero-shot image classification tasks. |
VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding (2021.emnlp-main)
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Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, Christoph Feichtenhofer
| Challenge: | Recent work adopts a "pre-training + fine-tuning" approach for zero-shot transfer to end tasks without fine- tuning. |
| Approach: | They propose a contrastive approach to pre-train a transformer model for zero-shot video and text understanding without using any labels on downstream tasks. |
| Outcome: | The proposed model outperforms supervised approaches on downstream tasks and outperformed previous approaches. |