Summary of Multimodal Data Curation Via Object Detection and Filter Ensembles, by Tzu-heng Huang et al.
Multimodal Data Curation via Object Detection and Filter Ensembles
by Tzu-Heng Huang, Changho Shin, Sui Jiet Tay, Dyah Adila, Frederic Sala
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach combines object detection and weak supervision-based ensembling to curate multimodal data for the DataComp competition’s filtering track. The technique involves two steps: first, an out-of-the-box zero-shot object detection model is used to extract granular information and produce filter designs; second, weak supervision is employed to ensemble filtering rules. This approach achieves a 4% performance improvement over the best-performing baseline in the small scale track and a 4.2% improvement in the medium scale track by ensembling existing baselines with weak supervision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed approach uses object detection and weak supervision to curate multimodal data for the DataComp competition. The technique involves two steps: first, an out-of-the-box zero-shot object detection model is used; second, weak supervision is employed to ensemble filtering rules. This approach does well in both small and medium scale tracks. |
Keywords
* Artificial intelligence * Object detection * Zero shot
