Summary of When Does Perceptual Alignment Benefit Vision Representations?, by Shobhita Sundaram et al.
When Does Perceptual Alignment Benefit Vision Representations?
by Shobhita Sundaram, Stephanie Fu, Lukas Muttenthaler, Netanel Y. Tamir, Lucy Chai, Simon Kornblith, Trevor Darrell, Phillip Isola
First submitted to arxiv on: 14 Oct 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 paper investigates how aligning computer vision model representations with human perceptual judgments impacts their usability across various tasks, including image generation, object detection, and scene understanding. The authors finetune state-of-the-art models on human similarity judgments for image triplets and evaluate them on standard vision benchmarks like ImageNet, COCO, and KITTI. They find that aligning models to perceptual judgments yields better representations that improve performance across many tasks, including counting, segmentation, depth estimation, instance retrieval, and retrieval-augmented generation. Furthermore, the aligned models perform well in out-of-distribution domains such as medical imaging and 3D environment frames. This work demonstrates the importance of incorporating human perceptual knowledge into vision models to create more effective representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to make a computer see the world like humans do. Right now, computers don’t always understand what makes one picture similar to another. They might focus on wrong things or miss important details. This paper tries to change that by making computers learn from how humans decide if two pictures are similar or not. The researchers take existing computer vision models and adjust them to match human judgments about image triplets. Then, they test these adjusted models on various tasks like counting objects, recognizing scenes, and understanding depth. They find that the adjusted models perform better than before across many tasks, even when dealing with unusual images like medical X-rays or 3D environments. |
Keywords
» Artificial intelligence » Depth estimation » Image generation » Object detection » Retrieval augmented generation » Scene understanding
