CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation

9 Mar 2023  ·  Shoukun Sun, Min Xian, Fei Xu, Tiankai Yao, Luca Capriotti ·

The click-based interactive segmentation aims to extract the object of interest from an image with the guidance of user clicks. Recent work has achieved great overall performance by employing the segmentation from the previous output. However, in most state-of-the-art approaches, 1) the inference stage involves inflexible heuristic rules and a separate refinement model; and 2) the training cannot balance the number of user clicks and model performance. To address the challenges, we propose a click-based and mask-guided interactive image segmentation framework containing three novel components: Cascade-Forward Refinement (CFR), Iterative Click Loss (ICL), and SUEM image augmentation. The proposed ICL allows model training to improve segmentation and reduce user interactions simultaneously. The CFR offers a unified inference framework to generate segmentation results in a coarse-to-fine manner. The proposed SUEM augmentation is a comprehensive way to create large and diverse training sets for interactive image segmentation. Extensive experiments demonstrate the state-of-the-art performance of the proposed approach on five public datasets. Remarkably, our model achieves an average of 2.9 and 7.5 clicks of NoC@95 on the Berkeley and DAVIS sets, respectively, improving by 33.2% and 15.5% over the previous state-of-the-art results. The code and trained model are available at https://github.com/TitorX/CFR-ICL-Interactive-Segmentation.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Interactive Segmentation Berkeley ICL CFR-1 (ViT-H, C+L) NoC@90 1.46 # 1
NoC@95 2.90 # 1
Interactive Segmentation DAVIS ICL CFR-1 (ViT-H, C+L) NoC@85 3 # 2
NoC@90 4.24 # 1
NoC@95 7.50 # 1
Interactive Segmentation GrabCut ICL CFR-1 (ViT-H, SBD) NoC@90 1.42 # 4
NoC@95 1.62 # 1
Interactive Segmentation GrabCut SimpleClick CFR-1 (ViT-H, SBD) NoC@85 1.30 # 2
NoC@90 1.32 # 2
NoC@95 1.78 # 2
Interactive Segmentation PASCAL VOC ICL CFR-1 (ViT-H, C+L) NoC@85 1.72 # 1
NoC@90 1.94 # 1
NoC@95 2.45 # 1
Interactive Segmentation SBD SimpleClick CFR-1 (ViT-H, SBD) NoC@85 2.45 # 1
NoC@90 4.08 # 1
NoC@95 9.80 # 1

Methods


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