Editing Computer Vision (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Human vision processes images in a hierarchical, parallel manner. CNNs were designed to mimic this: early layers detect simple features (edges, colors), middle layers combine these into shapes and textures, and later layers assemble these into object representations.

The key insight behind convolutions is '''translation invariance and parameter sharing'''. A filter that detects a horizontal edge in the top-left corner of an image should also detect it anywhere else. Sharing weights across spatial positions means the network learns this once, not for every possible location — vastly reducing parameters compared to a fully connected network.

'''Receptive field''': Each neuron in a deeper layer "sees" a larger portion of the original image. Stacking convolution layers increases the receptive field, allowing the network to integrate information over larger regions.

'''Residual connections''' (ResNets) solved the degradation problem: simply adding identity skip connections (output = F(x) + x) allowed training networks of 100+ layers by giving gradients a shortcut path backward, preventing vanishing.

'''Vision Transformers''' treat an image as a sequence of patches (e.g., 16×16 pixel patches), apply position embeddings, and process them with multi-head self-attention. This allows global context from the start — every patch attends to every other — unlike CNNs where large receptive fields only emerge deep in the network.
</div>

<div style="background-color: #8B0000; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">