Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning
May 3, 2021
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Tsung-Wei Ke
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Jyh-Jing Hwang
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About
Weakly supervised segmentation is challenging as sparsely labeled pixels do not provide sufficient supervision: A semantic segment may contain multiple distinctive regions whereas adjacent segments may appear similar. Common approaches use the few labeled pixels in all training images to train a segmentation model, and then propagate labels within each image based on visual or feature similarity. Instead, we treat segmentation as a semi-supervised pixel-wise metric learning problem, where pixels in different segments are mapped to distinctive features. Naturally, our unlabeled pixels participate not only in data-driven grouping within each image, but also in discriminative feature learning within and across images. Our results on Pascal VOC and DensePose datasets demonstrate our substantial accuracy gain on various forms of weak supervision including image-level tags, bounding boxes, labeled points, and scribbles.Weakly supervised segmentation is challenging as sparsely labeled pixels do not provide sufficient supervision: A semantic segment may contain multiple distinctive regions whereas adjacent segments may appear similar. Common approaches use the few labeled pixels in all training images to train a segmentation model, and then propagate labels within each image based on visual or feature similarity. Instead, we treat segmentation as a semi-supervised pixel-wise metric learning problem, where pixels…
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About ICLR 2021
The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.
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