**ARShoe** is a multi-branch network for pose estimation and segmentation tackling the "try-on" problem for augmented reality shoes. Consisting of an encoder and a decoder, the multi-branch network is trained to predict keypoints [heatmap](https://paperswithcode.com/method/heatmap) (heatmap), [PAFs](https://paperswithcode.com/method/pafs) heatmap (pafmap), and segmentation results (segmap) simultaneously. Post processes are then performed for a smooth and realistic virtual try-on.

**Zero Redundancy Optimizer (ZeRO)** is a sharded data parallel method for distributed training. ZeRODP removes the memory state redundancies across data-parallel processes by partitioning the model states instead of replicating them, and it retains the compute/communication efficiency by retaining the computational granularity and communication volume of DP using a dynamic communication schedule during training.

ZeRO

ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

ARShoe

ARShoe: Real-Time Augmented Reality Shoe Try-on System on Smartphones

**Single-Shot Multi-Object Tracker** or **SMOT**, is a tracking framework that converts any single-shot detector (SSD) model into an online multiple object tracker, which emphasizes simultaneously detecting and tracking of the object paths. Contrary to the existing tracking by detection approaches which suffer from errors made by the object detectors, SMOT adopts the recently proposed scheme of tracking by re-detection.

The proposed SMOT consists of two stages. The first stage generates temporally consecutive tracklets by exploring the temporal and spatial correlations from previous frame. The second stage performs online linking of the tracklets to generate a face track for each person (better view in color).

Source	ARShoe: Real-Time Augmented Reality Shoe Try-on System on Smartphones
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com