18 citations · 46 across the 8 of their papers we have counts for
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cs.CV2023★ 18 cited
Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation
Peng-Tao Jiang, Yuqi Yang
Weakly supervised semantic segmentation with weak labels is a long-lived ill-posed problem. Mainstream methods mainly focus on improving the quality of pseudo labels. In this repor…
cs.CV2023
Robust Meta Learning for Image based tasks
Penghao Jiang, Xin Ke, ZiFeng Wang +1
A machine learning model that generalizes well should obtain low errors on unseen test examples. Thus, if we learn an optimal model in training data, it could have better generaliz…
cs.LG2023★ 1 cited
Invariant Meta Learning for Out-of-Distribution Generalization
Penghao Jiang, Ke Xin, Zifeng Wang +1
Modern deep learning techniques have illustrated their excellent capabilities in many areas, but relies on large training data. Optimization-based meta-learning train a model on a…