Table of Links
-
Related work
-
Our method
D. Visualization
D.1. Selected and Discarded Samples
We show some samples selected and discarded by our method in Figure 7. Our proposed method is able to select high-quality samples (best sample) while filtering out low-quality samples (worst sample), which can effectively improve the data learning efficiency of the model. For example, our method is capable of identifying accurately segmented data for applesauce. In cases where applesauce is not present in the generated raw image or is not encompassed within the segmentation mask, our method can discard such samples. For alarm clocks, our method tends to choose images with more complex appearances.
D.2. Instance Augmentation
We present some augmented data in Figure 8. By randomly pasting generated samples onto the LVIS training set, we effectively enrich the complexity of the scenes and thus increase the model’s learning efficiency on the generated data.
Authors:
(1) Muzhi Zhu, with equal contribution from Zhejiang University, China;
(2) Chengxiang Fan, with equal contribution from Zhejiang University, China;
(3) Hao Chen, Zhejiang University, China ([email protected]);
(4) Yang Liu, Zhejiang University, China;
(5) Weian Mao, Zhejiang University, China and The University of Adelaide, Australia;
(6) Xiaogang Xu, Zhejiang University, China;
(7) Chunhua Shen, Zhejiang University, China ([email protected]).
This paper is