Reduces intra-class variance without significant computational overhead, making data points from the same class closer in the feature space. 2. Depth Awareness and Learnable Feature Fusion This technique embeds 3D geometry directly into CNNs.
Lower-scale inputs can be concatenated to the output of convolutional layers, reinforcing multi-scale features. With/In
Depth features are integrated directly into standard feature maps, helping the network understand structure. helping the network understand structure.