Embedded Dot Product Affinity is a type of affinity or self-similarity function between two points $\mathbb{x_{i}}$ and $\mathbb{x_{j}}$ that uses a dot product function in an embedding space:
$$ f\left(\mathbb{x_{i}}, \mathbb{x_{j}}\right) = \theta\left(\mathbb{x_{i}}\right)^{T}\phi\left(\mathbb{x_{j}}\right) $$
Here $\theta\left(x_{i}\right) = W_{θ}x_{i}$ and $\phi\left(x_{j}\right) = W_{φ}x_{j}$ are two embeddings.
The main difference between the dot product and embedded Gaussian affinity functions is the presence of softmax, which plays the role of an activation function.
Source: Non-local Neural NetworksPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Action Classification | 1 | 14.29% |
Action Recognition | 1 | 14.29% |
Instance Segmentation | 1 | 14.29% |
Keypoint Detection | 1 | 14.29% |
Object Detection | 1 | 14.29% |
Pose Estimation | 1 | 14.29% |
Video Classification | 1 | 14.29% |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |