  We present a systematic study on a new task called dichotomous image
segmentation (DIS) , which aims to segment highly accurate objects from natural
images. To this end, we collected the first large-scale DIS dataset, called
DIS5K, which contains 5,470 high-resolution (e.g., 2K, 4K or larger) images
covering camouflaged, salient, or meticulous objects in various backgrounds.
DIS is annotated with extremely fine-grained labels. Besides, we introduce a
simple intermediate supervision baseline (IS-Net) using both feature-level and
mask-level guidance for DIS model training. IS-Net outperforms various
cutting-edge baselines on the proposed DIS5K, making it a general self-learned
supervision network that can facilitate future research in DIS. Further, we
design a new metric called human correction efforts (HCE) which approximates
the number of mouse clicking operations required to correct the false positives
and false negatives. HCE is utilized to measure the gap between models and
real-world applications and thus can complement existing metrics. Finally, we
conduct the largest-scale benchmark, evaluating 16 representative segmentation
models, providing a more insightful discussion regarding object complexities,
and showing several potential applications (e.g., background removal, art
design, 3D reconstruction). Hoping these efforts can open up promising
directions for both academic and industries. Project page:
https://xuebinqin.github.io/dis/index.html.
