Summary of Dmt-jepa: Discriminative Masked Targets For Joint-embedding Predictive Architecture, by Shentong Mo et al.
DMT-JEPA: Discriminative Masked Targets for Joint-Embedding Predictive Architecture
by Shentong Mo, Sukmin Yun
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 DMT-JEPA model addresses the limitations of the joint-embedding predictive architecture (JEPA) by introducing a novel masked modeling objective that generates discriminative latent targets from neighboring information. This is achieved by computing feature similarities between each masked patch and its corresponding neighboring patches, selecting those with semantically meaningful relations, and aggregating their features using lightweight cross-attention heads. The resulting model demonstrates strong discriminative power, outperforming JEPA across various visual benchmarks, including image classification, semantic segmentation, and object detection tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces DMT-JEPA, a new model that solves the problem with JEPA’s understanding of local semantics. It does this by using neighboring patches to create targets for masked patches. The neighbors are chosen based on how similar their features are to the masked patch’s features. This helps the model keep track of important details in the images. The paper shows that DMT-JEPA works well on several different tasks, like classifying images and identifying objects. |
Keywords
» Artificial intelligence » Cross attention » Embedding » Image classification » Object detection » Semantic segmentation » Semantics
