Summary of Dilated Convolution with Learnable Spacings Makes Visual Models More Aligned with Humans: a Grad-cam Study, by Rabih Chamas et al.
Dilated Convolution with Learnable Spacings makes visual models more aligned with humans: a Grad-CAM study
by Rabih Chamas, Ismail Khalfaoui-Hassani, Timothee Masquelier
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Dilated Convolution with Learnable Spacing (DCLS) is a novel advanced convolution method that expands receptive fields without increasing parameters, outperforming standard and dilated convolutions on computer vision benchmarks. This paper explores the interpretability of DCLS, defined as alignment with human visual strategies, using Spearman correlation between GradCAM heatmaps and ClickMe dataset heatmaps. The authors replace standard convolution layers in eight reference models (ResNet50, ConvNeXt, CAFormer, ConvFormer, FastViT) with DCLS, improving interpretability scores in seven models. They also introduce Threshold-Grad-CAM to enhance interpretability across most models. This study demonstrates the effectiveness of DCLS in increasing model interpretability and provides code and checkpoints for reproduction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DCLS is a new way to make computer vision models work better without adding more parts. It looks at how human eyes move when we look at things and tries to match that. The paper shows that DCLS makes some old models (like ResNet50) understand what they’re looking at better. They also made a new tool called Threshold-Grad-CAM that helps all the models see what they’re looking at clearly. |
Keywords
» Artificial intelligence » Alignment
