292 citations · 507 across the 24 of their papers we have counts for
27 papers
CRF Loss is How Networks Should Learn Boundaries in Weakly Supervised Segmentation
Joshua Li, Yuri Boykov
Weakly Supervised Semantic Segmentation (WSSS) learns pixel-level predictions from image-level tags. Recent work focuses on improving coarse CAMs extracted from large vision-langua…
Occlusion-Ordered Semantic Instance Segmentation
Soroosh Baselizadeh, Cheuk-To Yu, Olga Veksler +1
Standard semantic instance segmentation provides useful, but inherently 2D information from a single image. To enable 3D analysis, one usually integrates absolute monocular depth e…
Approximate Size Targets Are Sufficient for Accurate Semantic Segmentation
Xingye Fan, Zhongwen, Zhang +1
This paper demonstrates a surprising result for segmentation with image-level targets: extending binary class tags to approximate relative object-size distributions allows off-the-…
Collision Cross-entropy for Soft Class Labels and Deep Clustering
Zhongwen Zhang, Yuri Boykov
We propose "collision cross-entropy" as a robust alternative to Shannon's cross-entropy (CE) loss when class labels are represented by soft categorical distributions y. In general,…
Discriminative Entropy Clustering and its Relation to K-means and SVM
Zhongwen Zhang, Yuri Boykov
Maximization of mutual information between the model's input and output is formally related to "decisiveness" and "fairness" of the softmax predictions, motivating these unsupervis…
Robust Trust Region for Weakly Supervised Segmentation
Dmitrii Marin, Yuri Boykov
Acquisition of training data for the standard semantic segmentation is expensive if requiring that each pixel is labeled. Yet, current methods significantly deteriorate in weakly s…