DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation
We present DFormer, a novel RGB-D pretraining framework to learn transferable representations for RGB-D segmentation tasks. DFormer has two new key innovations: 1) Unlike previous works that encode RGB-D information with RGB pretrained backbone, we pretrain the backbone using image-depth pairs from ImageNet-1K, and hence the DFormer is endowed with the capacity to encode RGB-D representations; 2) DFormer comprises a sequence of RGB-D blocks, which are tailored for encoding both RGB and depth information through a novel building block design. DFormer avoids the mismatched encoding of the 3D geometry relationships in depth maps by RGB pretrained backbones, which widely lies in existing methods but has not been resolved. We finetune the pretrained DFormer on two popular RGB-D tasks, i.e., RGB-D semantic segmentation and RGB-D salient object detection, with a lightweight decoder head. Experimental results show that our DFormer achieves new state-of-the-art performance on these two tasks with less than half of the computational cost of the current best methods on two RGB-D semantic segmentation datasets and five RGB-D salient object detection datasets. Our code is available at: https://github.com/VCIP-RGBD/DFormer.
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NYUv2
SUN RGB-D
KITTI-360
NLPR
SIP
NJU2K
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Uses Extra Training Data |
Benchmark |
|---|---|---|---|---|---|---|---|
| RGB-D Salient Object Detection | DES | DFormer-L | S-Measure | 94.8 | # 1 | ||
| Average MAE | 0.013 | # 1 | |||||
| max E-Measure | 98.0 | # 1 | |||||
| max F-Measure | 95.6 | # 1 | |||||
| RGB-D Salient Object Detection | NJU2K | DFormer-L | S-Measure | 93.7 | # 1 | ||
| Average MAE | 0.023 | # 1 | |||||
| max E-Measure | 96.4 | # 1 | |||||
| max F-Measure | 94.6 | # 1 | |||||
| RGB-D Salient Object Detection | NLPR | DFormer-L | S-Measure | 94.2 | # 1 | ||
| Average MAE | 0.016 | # 1 | |||||
| max F-Measure | 93.9 | # 1 | |||||
| max E-Measure | 97.1 | # 1 | |||||
| Semantic Segmentation | NYU Depth v2 | DFormer-L | Mean IoU | 57.2% | # 6 | ||
| Semantic Segmentation | NYU Depth v2 | DFormer-T | Mean IoU | 51.8% | # 38 | ||
| Semantic Segmentation | NYU Depth v2 | DFormer-S | Mean IoU | 53.6% | # 21 | ||
| Semantic Segmentation | NYU Depth v2 | DFormer-B | Mean IoU | 55.6% | # 13 | ||
| RGB-D Salient Object Detection | SIP | DFormer-L | S-Measure | 91.5 | # 1 | ||
| max E-Measure | 95.0 | # 1 | |||||
| max F-Measure | 93.8 | # 1 | |||||
| Average MAE | 0.032 | # 1 | |||||
| RGB-D Salient Object Detection | STERE | DFormer-L | S-Measure | 92.3 | # 1 | ||
| Average MAE | 0.030 | # 1 | |||||
| max F-Measure | 92.9 | # 1 | |||||
| max E-Measure | 95.2 | # 1 | |||||
| Semantic Segmentation | SUN-RGBD | DFormer-L | Mean IoU | 52.5% | # 3 | ||
| Semantic Segmentation | SUN-RGBD | DFormer-B | Mean IoU | 51.2% | # 7 | ||
| Semantic Segmentation | SUN-RGBD | TokenFusion (S) | Mean IoU | 50.0% | # 11 | ||
| Semantic Segmentation | SUN-RGBD | FSFNet | Mean IoU | 48.8% | # 19 |
