Summary of The Progression Of Transformers From Language to Vision to Mot: a Literature Review on Multi-object Tracking with Transformers, by Abhi Kamboj

Main: Computer Vision and Pattern Recognition (cs.CV)
Secondary: Artificial Intelligence (cs.AI)

The Progression of Transformers from Language to Vision to MOT: A Literature Review on Multi-Object Tracking with Transformers

by Abhi Kamboj

First submitted to arxiv on: 24 Jun 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary Transformers have revolutionized natural language processing since their introduction with machine translation applications. Recently, they’ve been applied to computer vision tasks, achieving significant progress. This literature review highlights major advances in computer vision utilizing transformers and focuses on Multi-Object Tracking (MOT), where transformers are becoming increasingly competitive in state-of-the-art MOT works. While still lagging behind traditional deep learning methods, transformers have made notable strides.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Transformers were originally designed for machine translation but changed the game in natural language processing. Now, they’re helping computers see better too! Researchers looked at how transformers did in computer vision tasks and found that they’re getting really good at tracking multiple objects. While still not as strong as older methods, transformers are a step closer to catching up.