  Weakly-supervised semantic segmentation aims to reduce labeling costs by
training semantic segmentation models using weak supervision, such as
image-level class labels. However, most approaches struggle to produce accurate
localization maps and suffer from false predictions in class-related
backgrounds (i.e., biased objects), such as detecting a railroad with the train
class. Recent methods that remove biased objects require additional supervision
for manually identifying biased objects for each problematic class and
collecting their datasets by reviewing predictions, limiting their
applicability to the real-world dataset with multiple labels and complex
relationships for biasing. Following the first observation that biased features
can be separated and eliminated by matching biased objects with backgrounds in
the same dataset, we propose a fully-automatic/model-agnostic biased removal
framework called MARS (Model-Agnostic biased object Removal without additional
Supervision), which utilizes semantically consistent features of an
unsupervised technique to eliminate biased objects in pseudo labels.
Surprisingly, we show that MARS achieves new state-of-the-art results on two
popular benchmarks, PASCAL VOC 2012 (val: 77.7%, test: 77.2%) and MS COCO 2014
(val: 49.4%), by consistently improving the performance of various WSSS models
by at least 30% without additional supervision.
