Summary of Adaptive Masking Enhances Visual Grounding, by Sen Jia et al.
Adaptive Masking Enhances Visual Grounding
by Sen Jia, Lei Li
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A recent surge in zero-shot and few-shot learning in visual grounding is driven by the success of large-scale vision-language pre-training on datasets like LAION-5B and DataComp-1B. However, this expansion creates a bottleneck in advancing low-shot learning capabilities due to data availability and computational overhead challenges. To address this, we propose IMAGE, an interpretative masked autoencoder method that leverages adaptive masking on salient regions of the feature maps generated by the vision backbone. This approach enables robust representation learning through occluded information reconstruction, facilitating attention to both local and global features. We evaluate our method on COCO and ODinW benchmark datasets, demonstrating superior performance in zero-shot and few-shot tasks. Experimental results show that IMAGE outperforms baseline models, achieving enhanced generalization and improved performance in low-shot scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to improve visual grounding without needing huge amounts of data. Right now, most progress in this area is due to using large datasets like LAION-5B and DataComp-1B. However, this requires a lot of computational power and data storage, which can be a problem. To solve this issue, the authors propose a new method called IMAGE that uses clever masking techniques to learn more about images without needing as much data. They tested their approach on some benchmark datasets and found it worked better than other methods in zero-shot and few-shot learning tasks. |
Keywords
» Artificial intelligence » Attention » Autoencoder » Few shot » Generalization » Grounding » Representation learning » Zero shot
