Summary of Weak-to-strong Compositional Learning From Generative Models For Language-based Object Detection, by Kwanyong Park et al.
Weak-to-Strong Compositional Learning from Generative Models for Language-based Object Detection
by Kwanyong Park, Kuniaki Saito, Donghyun Kim
First submitted to arxiv on: 21 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 This paper presents a novel method for generating structured synthetic data that enhances the compositional understanding of vision-language (VL) models in language-based object detection tasks. The proposed framework, called “Weak-to-Strong Compositional Learning” (WSCL), generates densely paired positive and negative triplets consisting of images, text descriptions, and bounding boxes. By leveraging these synthetic triplets, the paper shows that VL models can be transformed from weaker to stronger in terms of compositional understanding, leading to significant performance boosts in benchmarks such as Omnilabel (+5AP) and D3 (+6.9AP). The approach relies on generative foundational models’ exceptional compositional understanding capabilities and a new compositional contrastive learning formulation that discovers semantics and structures in complex descriptions from synthetic triplets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand complex sentences about objects better. Right now, computer vision models struggle to comprehend descriptions like “the cat sitting on the mat”. To fix this, researchers came up with a new way to create fake data that makes these models smarter. They created pairs of images and text descriptions, along with bounding boxes, which are like labels that show where objects are in an image. By using this fake data, they were able to improve the performance of language-based object detection tasks by up to 6.9 percentage points. |
Keywords
» Artificial intelligence » Object detection » Semantics » Synthetic data
