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Bi-Directional Relationship Inferring Network for Referring Image Segmentation

CVPR 2020 · Vision-language and multimodal › Cross-modal learning · approach: Contrastive vision-language alignment · 209 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add bi-directional cross-modal attention and gated multi-level fusion (ablation)DeepLab ResNet-101v2 referring segmentation network · UNC valoverall IoU: 52.26 -> 61.35Helped
Add vision-guided linguistic attention to the language-guided visual relationship module (ablation)BCAM · UNC valoverall IoU: 55.14 -> 56.76Helped
Add language guidance to visual relationship modeling (ablation)BCAM · UNC valoverall IoU: 53.91 -> 56.76Helped
Use gated top-down and bottom-up fusion rather than direct feature summation (ablation)BRINet · UNC valoverall IoU: 59.59 -> 61.35Helped
Combine top-down and bottom-up fusion instead of using either direction alone (ablation)BRINet · UNC valoverall IoU: 60.32 -> 61.35; 60.55 -> 61.35Helped
Use three feature scales rather than f4 + f5 (ablation)BRINet · UNC valoverall IoU: 60.09 -> 61.35Helped
Use GBFM rather than ConvLSTM for multi-level feature fusion (ablation)BRINet · UNC valoverall IoU: 58.78 -> 61.35Helped
Add spatial coordinate features (ablation)BRINet · UNC valoverall IoU: 58.91 -> 61.35Helped
Add DenseCRF post-processingBRINet · ReferIt testoverall IoU: 63.11 -> 63.46Helped
Add DenseCRF post-processingBRINet · UNC testAoverall IoU: 62.99 -> 63.37No clear effect
Add DenseCRF post-processingBRINet · UNC+ testBoverall IoU: 42.11 -> 42.13No clear effect
Use BRINet rather than STEPReferring image segmentation · UNC testAoverall IoU: 63.46 -> 62.99No clear effect

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