# PatchBatch: a Batch Augmented Loss for Optical Flow

arXiv: https://arxiv.org/abs/1512.01815 · HTML: https://mlsanity.com/paper/1512.01815/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2015.

## Built on by (most cited first, 3 of 3)

- [Dense Continuous-Time Optical Flow from Events and Frames](https://mlsanity.com/paper/2203.13674/) (2022)
- [Target Detection and Segmentation in Circular-Scan Synthetic-Aperture-Sonar Images using Semi-Supervised Convolutional Encoder-Decoders](https://mlsanity.com/paper/2101.03603/) (2021)
- [External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery](https://mlsanity.com/paper/2107.10504/) (2021)

## Abstract (arXiv)

We propose a new pipeline for optical flow computation, based on Deep Learning techniques. We suggest using a Siamese CNN to independently, and in parallel, compute the descriptors of both images. The learned descriptors are then compared efficiently using the L2 norm and do not require network processing of patch pairs. The success of the method is based on an innovative loss function that computes higher moments of the loss distributions for each training batch. Combined with an Approximate Nearest Neighbor patch matching method and a flow interpolation technique, state of the art performance is obtained on the most challenging and competitive optical flow benchmarks.