Patchdrivenet May 2026

We often view progress as a series of "patches"—quick fixes for systemic bugs, temporary bridges across widening digital divides. But what if the patch isn't the fix? What if the patch is the network?

: The patch-driven approach makes the model more resilient to occlusions or image corruption, as the network can still identify objects based on the remaining visible patches. Scalability patchdrivenet

Architecture of Patch-Driven-Net

2.4 Temporal Patch Propagation

To leverage video streams, PatchDriveNet reuses patch embeddings from the previous frame using a lightweight optical flow predictor. Only patches with significant motion (displacement >3 pixels) are recomputed – reducing redundant computation by up to 65%. We often view progress as a series of

  1. Patch Extraction: The input image is divided into non-overlapping patches, which are then fed into the network.
  2. Patch Embedding: Each patch is embedded into a higher-dimensional space using a learnable embedding layer.
  3. Patch Processing: The embedded patches are processed independently using a series of convolutional and activation layers.
  4. Patch Aggregation: The processed patches are aggregated using a combination of concatenation and convolutional layers.
  1. Patch Extraction Module: This module divides the input image into smaller patches, which are then fed into the network for processing.
  2. Patch Embedding Module: This module applies a set of learnable transformations to each patch to extract relevant features, which are then aggregated to form a patch-wise representation.
  3. Patch Interaction Module: This module enables the exchange of information between patches, allowing the network to capture long-range dependencies and contextual relationships.
  4. Global Aggregation Module: This module aggregates the patch-wise representations to form a comprehensive representation of the input image.

Limitations