Summary of Grounding Descriptions in Images Informs Zero-shot Visual Recognition, by Shaunak Halbe et al.
Grounding Descriptions in Images informs Zero-Shot Visual Recognition
by Shaunak Halbe, Junjiao Tian, K J Joseph, James Seale Smith, Katherine Stevo, Vineeth N Balasubramanian, Zsolt Kira
First submitted to arxiv on: 5 Dec 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 This research proposes a new pretraining strategy for vision-language models (VLMs) like CLIP to improve zero-shot visual recognition on open-vocabulary concepts. The current method struggles with identifying fine-grained entities and generalizing to unseen concepts, which are limited by the training distribution. To address these challenges, the authors develop GRAIN, a new approach that learns to jointly ground textual descriptions in image regions and align overarching captions with global image representations. This is achieved by leveraging frozen Multimodal Large Language Models (MLLMs) to derive large-scale synthetic annotations. The proposed model demonstrates enhanced zero-shot performance compared to current state-of-the-art methods across 11 diverse image classification datasets, including the newly curated Products-2023 dataset. Additionally, the authors showcase the model’s ability to recognize novel concepts by benchmarking on Products-2023. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Vision-language models (VLMs) like CLIP are good at recognizing things in pictures, but they struggle with small details and new ideas. The authors of this paper want to make VLMs better. They propose a new way to train VLMs called GRAIN. GRAIN helps VLMs understand what’s in a picture and what it means. It does this by matching words to parts of the picture and matching overall descriptions to the whole picture. The authors test GRAIN on 11 different datasets and show that it works better than other methods. They also create a new dataset called Products-2023, which has many new and unusual concepts for VLMs to learn. |
Keywords
» Artificial intelligence » Image classification » Pretraining » Zero shot
