  Learning from imperfect data becomes an issue in many industrial applications
after the research community has made profound progress in supervised learning
from perfectly annotated datasets. The purpose of the Learning from Imperfect
Data (LID) workshop is to inspire and facilitate the research in developing
novel approaches that would harness the imperfect data and improve the
data-efficiency during training. A massive amount of user-generated data
nowadays available on multiple internet services. How to leverage those and
improve the machine learning models is a high impact problem. We organize the
challenges in conjunction with the workshop. The goal of these challenges is to
find the state-of-the-art approaches in the weakly supervised learning setting
for object detection, semantic segmentation, and scene parsing. There are three
tracks in the challenge, i.e., weakly supervised semantic segmentation (Track
1), weakly supervised scene parsing (Track 2), and weakly supervised object
localization (Track 3). In Track 1, based on ILSVRC DET, we provide pixel-level
annotations of 15K images from 200 categories for evaluation. In Track 2, we
provide point-based annotations for the training set of ADE20K. In Track 3,
based on ILSVRC CLS-LOC, we provide pixel-level annotations of 44,271 images
for evaluation. Besides, we further introduce a new evaluation metric proposed
by \cite{zhang2020rethinking}, i.e., IoU curve, to measure the quality of the
generated object localization maps. This technical report summarizes the
highlights from the challenge. The challenge submission server and the
leaderboard will continue to open for the researchers who are interested in it.
More details regarding the challenge and the benchmarks are available at
https://lidchallenge.github.io
