**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).

**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.

ARShoe

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

SMOT

SMOT: Single-Shot Multi Object Tracking

The Segment Anything Model (SAM) shows remarkable segmentation ability with sparse prompts like points. However, manual prompt is not always feasible, as it may not be accessible in real-world application. In this work, we aim to eliminate the need for manual prompt.The key idea is to employ Cross-modal Chains of Thought Prompting (CCTP) to reason visual prompts using the semantic information given by a generic text prompt. We introduce a test-time adaptation per-instance mechanism called Generalizable SAM (GenSAM) to automatically generate and optimize visual prompts the generic task prompt.  CCTP maps a single generic text prompt onto image-specific consensus foreground and background heatmaps using vision-language models, acquiring reliable visual prompts. Moreover, to test-time adapt the visual prompts, we further propose Progressive Mask Generation (PMG) to iteratively reweight the input image, guiding the model to focus on the targets in a coarse-to-fine manner.Crucially, all network parameters are fixed, avoiding the need for additional training.Experiments demonstrate the superiority of GenSAM. Experiments on three benchmarks demonstrate that GenSAM outperforms point supervision approaches and achieves comparable results to scribble supervision ones, solely relying on general task descriptions as prompts.

Source	SMOT: Single-Shot Multi Object Tracking
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com