About
We propose this "Computer Vision in the Wild (CVinW)" workshop (computer-vision-in-the-wild.github.io/eccv-2022/), aiming to gather academic and industry communities to work on CV problems in real-world scenarios, focusing on the challenge of open-set/domain visual recognition and efficient task-level transfer. Since there is no established benchmarks to measure the progress of "CV in the Wild", we develop new benchmarks for image classification and object detection, to measure the task-level transfer ablity of various models/methods over diverse real-world datasets, in terms of both prediction accuracy and adaption efficiency. This workshop will also host two challenges based on the ELEVATER benchmarks (computer-vision-in-the-wild.github.io/ELEVATER/).
For those who are new to this topic, please check out the CVinW Reading List: github.com/Computer-Vision-in-the-Wild/CVinW_Readi…
We propose this "Computer Vision in the Wild (CVinW)" workshop (computer-vision-in-the-wild.github.io/eccv-2022/), aiming to gather academic and industry communities to work on CV problems in real-world scenarios, focusing on the challenge of open-set/domain visual recognition and efficient task-level transfer. Since there is no established benchmarks to measure the progress of "CV in the Wild", we develop new benchmarks for image classification and object detection, to measure the task-level transfer ablity of various models/methods over diverse real-world datasets, in terms of both prediction accuracy and adaption efficiency. This workshop will also host two challenges based on the ELEVATER benchmarks (computer-vision-in-the-wild.github.io/ELEVATER/).
For those who are new to this topic, please check out the CVinW Reading List: github.com/Computer-Vision-in-the-Wild/CVinW_Readi…