Loading Now

Summary of Perception Test 2023: a Summary Of the First Challenge and Outcome, by Joseph Heyward et al.


Perception Test 2023: A Summary of the First Challenge And Outcome

by Joseph Heyward, João Carreira, Dima Damen, Andrew Zisserman, Viorica Pătrăucean

First submitted to arxiv on: 20 Dec 2023

Categories

  • Main: Computer Vision and Pattern Recognition (cs.CV)
  • Secondary: None

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The First Perception Test challenge is a benchmarking effort that evaluates state-of-the-art video models on the Perception Test benchmark. The challenge features six tracks covering low-level and high-level tasks across various modalities, including object tracking, point tracking, temporal action localization, and more. Participants can interact with the challenges using either a language interface or a non-language interface. This report summarizes the task descriptions, metrics, baselines, and results of the challenge.
Low GrooveSquid.com (original content) Low Difficulty Summary
The First Perception Test challenge is a way to test how well video models work on videos. It has many different tasks that check things like object tracking and sound localization. You can use either words or pictures to interact with the challenges. The goal is to see which video models are best at doing these tasks.

Keywords

* Artificial intelligence  * Object tracking  * Tracking