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Encoder Fusion Network With Co-Attention Embedding for Referring Image Segmentation

CVPR 2021 · Vision-language and multimodal › Cross-modal learning · approach: Multi-modality image fusion · 179 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace decoder fusion with encoder fusion (ablation)CEFNet · UNC valoverall IoU: 52.86 -> 55.87Helped
Add vanilla co-attention to EFN (ablation)CEFNet · UNC valoverall IoU: 55.87 -> 59.65Helped
Add asymmetric co-attention to EFN (ablation)CEFNet · UNC valoverall IoU: 55.87 -> 60.09Helped
Add BEM to EFN with vanilla co-attention (ablation)CEFNet · UNC valoverall IoU: 59.65 -> 62.53Helped
Add BEM to EFN with asymmetric co-attention (ablation)CEFNet · UNC valoverall IoU: 60.09 -> 62.76Helped
Use ACM instead of VCM in the full modelCEFNet · UNC+ testBIoU: 43.50 -> 43.01Hurt
Train on combined UNC, UNC+ and G-Ref dataCEFNet · UNC valIoU: 62.76 -> 68.97Helped
Use ACM instead of VCM in the full modelCEFNet · UNC valIoU: 62.53 -> 62.76Helped
Use ACM instead of VCM in the full modelCEFNet · G-Ref valIoU: 51.22 -> 51.93Helped

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