SlowGuess/NeurIPS_2023_OCR huggingface dataset:
In this section, we provide more visual results in semantic segmentation, instance segmentation, and dichotomous image segmentation. Fig. 5 shows the comparisons of SegRefiner and other models (including instance segmentation models and refinement models) on COCO [34] validation set. Fig. 6 shows more comparisons between the coarse masks and refined masks on COCO validation set. These results demonstrate that the proposed SegRfiner can robustly correct inaccurate predictions in coarse masks. Fig. 7 and Fig. 8 show visual results on BIG dataset [12] and DIS5K dataset [40]. SegRefiner shows a strong capability for capturing extremely fine details on these two high-resolution datasets.
(b) Mask R-CNN [21]
(c) SOLO [51]
(d) QueryInst [18]
(e) CondInst [48]
Figure 5: Visual comparisons with other instance segmentation and refinement methods on COCO dataset. Our SegRefiner can robustly correct prediction errors both outside and inside the coarse mask. (Please refer to the next page for the remaining portion of this figure.)
(f) PointRend [29]
(g) RefineMask [57]
(h) Transfiner [27]
(i) Mask R-CNN + SegFix [56]
(j) Mask R-CNN + BPR [47]
Figure 5: Visual comparisons with other instance segmentation and refinement methods on COCO dataset. Our SegRefiner can robustly correct prediction errors both outside and inside the coarse mask.
(k) Mask R-CNN + SegRefiner (Ours)
(a) Coarse Mask
(b)Refined Mask
Figure 6: More visual results on COCO dataset. Coarse masks are obtained from Mask R-CNN [21]. Our SegRefiner corrects the errors of coarse masks (see Refined Mask).
(c)Refined Mask
Figure 7: More visual results on BIG dataset [12]. Coarse masks are obtained from Deeplab v3+ [5]. Our SegRefiner greatly enhances the mask quality (see Refined Mask). Please kindly zoom in for a better view.
(c) Refined Mask
Figure 8: More visual results on DIS5K dataset [40]. Coarse masks are obtained from ISNet [40]. Our SegRefiner captures extremely fine details (see Refined Mask). Please kindly zoom in for a better view.