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Weakly-Supervised Semantic Segmentation 벤치마크

Weakly-Supervised Semantic Segmentation on PASCAL VOC 2012 test

120개 결과 · ⬇ CSV · JSON

Mean IoU

63.8 68.58 73.35 78.12 82.9 2019-04 2026-09 IRNet (ResNet-50) — 64.8 (2019-04-10) IRNet (ResNet-50) — 64.8 (2019-04-10) SGAN — 67.2 (2019-10-12) SGAN — 67.2 (2019-10-12) LIID — 67.5 (2020-09-10) LIID — 67.5 (2020-09-10) EADER — 63.8 (2020-11-09) EADER — 63.8 (2020-11-09) GroupWSSS — 68.5 (2020-12-09) GroupWSSS — 68.5 (2020-12-09) Puzzle-CAM (ResNeSt-269) — 72.2 (2021-01-27) Puzzle-CAM (ResNeSt-101) — 67.7 (2021-01-27) Puzzle-CAM (ResNeSt-269) — 72.2 (2021-01-27) Puzzle-CAM (ResNeSt-101) — 67.7 (2021-01-27) DRS (DeepLabV2-R101) — 70.7 (2021-03-12) DRS (DeepLabV2-R101) — 70.7 (2021-03-12) WSGCN (MS-COCO-pre-trained weights) — 69.3 (2021-03-31) WSGCN (no Saliency map) — 68.8 (2021-03-31) WSGCN (MS-COCO-pre-trained weights) — 69.3 (2021-03-31) WSGCN (no Saliency map) — 68.8 (2021-03-31) SPML (DeepLabV2-R101) — 71.6 (2021-05-03) SPML (DeepLabV2-R101) — 71.6 (2021-05-03) EPS(DeepLabV1-ResNet101 — 71.8 (2021-05-19) EPS(DeepLabV2-ResNet101) — 70.8 (2021-05-19) EPS(DeepLabV1-ResNet101 — 71.8 (2021-05-19) EPS(DeepLabV2-ResNet101) — 70.8 (2021-05-19) CPN — 68.5 (2021-08-09) CPN — 68.5 (2021-08-09) PMM(Res2Net101, no saliency, no RW) — 70.5 (2021-08-30) PMM(ResNet38, no saliency, no RW) — 69.0 (2021-08-30) PMM(Res2Net101, no saliency, no RW) — 70.5 (2021-08-30) PMM(ResNet38, no saliency, no RW) — 69.0 (2021-08-30) ADELE (DeepLabV1-ResNet38) — 72.0 (2021-10-07) ADELE (DeepLabV1-ResNet38) — 72.0 (2021-10-07) RIB+Sal (DeepLabV2-ResNet101) — 70.0 (2021-10-13) RIB (DeepLabV2-ResNet101) — 68.6 (2021-10-13) RIB+Sal (DeepLabV2-ResNet101) — 70.0 (2021-10-13) RIB (DeepLabV2-ResNet101) — 68.6 (2021-10-13) PPC (w/ EPS) — 73.5 (2021-10-14) PPC (w/ EPS) — 73.5 (2021-10-14) Infer-CAM(DeepLabV2-R101) — 71.8 (2021-10-27) Infer-CAM(DeepLabV2-R101) — 71.8 (2021-10-27) GETAM(vitb-hybrid) — 72.3 (2021-12-06) GETAM(vitb-hybrid) — 72.3 (2021-12-06) URN(Res2Net-101, no saliency, no RW) — 71.5 (2021-12-14) URN(ScaleNet-101, no saliency, no RW) — 70.8 (2021-12-14) URN(ResNet-38, no saliency, no RW) — 70.6 (2021-12-14) URN(ResNet-101, no saliency, no RW) — 69.7 (2021-12-14) URN(Res2Net-101, no saliency, no RW) — 71.5 (2021-12-14) URN(ScaleNet-101, no saliency, no RW) — 70.8 (2021-12-14) URN(ResNet-38, no saliency, no RW) — 70.6 (2021-12-14) URN(ResNet-101, no saliency, no RW) — 69.7 (2021-12-14) VWL-L (EMANet) — 71.1 (2022-02-10) VWL-L — 70.7 (2022-02-10) VWL-M — 70.4 (2022-02-10) VWL-L (EMANet) — 71.1 (2022-02-10) VWL-L — 70.7 (2022-02-10) VWL-M — 70.4 (2022-02-10) SIPE (DeepLabV2-ResNet101, no saliency) — 69.7 (2022-03-06) SIPE (DeepLabV2-ResNet101, no saliency) — 69.7 (2022-03-06) W-OoD (WResNet-38) — 70.1 (2022-03-08) W-OoD (WResNet-38) — 70.1 (2022-03-08) RCA — 72.8 (2022-03-17) RCA — 72.8 (2022-03-17) SLRNet — 69.4 (2022-03-19) SLRNet(1-stage,ResNet38) — 67.6 (2022-03-19) SLRNet — 69.4 (2022-03-19) SLRNet(1-stage,ResNet38) — 67.6 (2022-03-19) ICAM — 70.8 (2022-03-23) ICAM — 70.8 (2022-03-23) AMN (DeepLabV2-ResNet101, MS-COCO-pretrained weights) — 70.6 (2022-03-30) AMN (DeepLabV2-ResNet101) — 69.6 (2022-03-30) AMN (DeepLabV2-ResNet101, MS-COCO-pretrained weights) — 70.6 (2022-03-30) AMN (DeepLabV2-ResNet101) — 69.6 (2022-03-30) L2G (ResNet101, DeepLab-LargeFOV) — 73.0 (2022-04-07) L2G (ResNet101, DeepLab-LargeFOV) — 73.0 (2022-04-07) RS+EPM (ResNet-101, multi-stage) — 73.6 (2022-04-14) RS+EPM (ResNet-50, single-stage) — 70.6 (2022-04-14) RS+EPM (ResNet-101, multi-stage) — 73.6 (2022-04-14) RS+EPM (ResNet-50, single-stage) — 70.6 (2022-04-14) ViT-PCM — 70.9 (2022-10-31) ViT-PCM — 70.9 (2022-10-31) ISIM (ResNeSt-200) — 74.98 (2022-11-22) ISIM (ResNet-101) — 71.45 (2022-11-22) ISIM (ResNeSt-200) — 74.98 (2022-11-22) ISIM (ResNet-101) — 71.45 (2022-11-22) CLIP-ES(DeepLabV2-ResNet101) — 73.9 (2022-12-16) CLIP-ES(DeepLabV2-ResNet101) — 73.9 (2022-12-16) BECO(DeepLabV3Plus+MiT-B2) — 73.5 (2023-01-01) BECO(DeepLabV3Plus+MiT-B2) — 73.5 (2023-01-01) WeakTr (ViT-S, multi-stage) — 79.0 (2023-04-03) WeakTr (DeiT-S, multi-stage) — 74.1 (2023-04-03) WeakTr (ViT-S, multi-stage) — 79.0 (2023-04-03) WeakTr (DeiT-S, multi-stage) — 74.1 (2023-04-03) MARS (ResNet-101, multi-stage) — 77.2 (2023-04-19) MARS (ResNet-101, multi-stage) — 77.2 (2023-04-19) WSSS-SAM(DeepLabV2-ResNet101) — 77.1 (2023-05-02) WSSS-SAM(DeepLabV2-ResNet101) — 77.1 (2023-05-02) HSC — 74.5 (2023-08-01) HSC — 74.5 (2023-08-01) ACR-WSSS(DeepLabV2-ResNet101) — 70.9 (2023-08-08) ACR-WSSS(DeepLabV2-ResNet101) — 70.9 (2023-08-08) T2MDiffusion(DeepLabV2-ResNet101) — 74.2 (2023-09-08) T2MDiffusion(DeepLabV2-ResNet101) — 74.2 (2023-09-08) FMA-WSSS (Swin-L) — 81.6 (2023-12-06) FMA-WSSS (Swin-L) — 81.6 (2023-12-06) ClusterCAM — 70.7 (2024-01-05) ClusterCAM — 70.7 (2024-01-05) QA-CLIMS — 75.5 (2024-01-18) QA-CLIMS — 75.5 (2024-01-18) SemPLeS (Swin-L) — 82.9 (2024-01-22) SFC(ResNet-101) — 72.5 (2024-01-22) SemPLeS (Swin-L) — 82.9 (2024-01-22) SFC(ResNet-101) — 72.5 (2024-01-22) DHR (Swin-L, Mask2Former) — 82.3 (2024-03-30) DHR (Swin-L, Mask2Former) — 82.3 (2024-03-30) FBR — 74.9 (2024-06-22) FBR — 74.9 (2024-06-22) ORANDNet — 72.9 (2024-06-28) ORANDNet — 72.9 (2024-06-28) IRNet (ResNet-50) — 64.8 (2019-04-10) SGAN — 67.2 (2019-10-12) LIID — 67.5 (2020-09-10) GroupWSSS — 68.5 (2020-12-09) Puzzle-CAM (ResNeSt-269) — 72.2 (2021-01-27) PPC (w/ EPS) — 73.5 (2021-10-14) RS+EPM (ResNet-101, multi-stage) — 73.6 (2022-04-14) ISIM (ResNeSt-200) — 74.98 (2022-11-22) WeakTr (ViT-S, multi-stage) — 79.0 (2023-04-03) FMA-WSSS (Swin-L) — 81.6 (2023-12-06) SemPLeS (Swin-L) — 82.9 (2024-01-22)
RankModel Mean IoU Extra Training Data PaperCodeYear
1 SemPLeS (Swin-L) 82.9 Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation nvlabs/semples 2024
2 DHR (Swin-L, Mask2Former) 82.3 DHR: Dual Features-Driven Hierarchical Rebalancing in Inter- and Intra-Class Regions for Weakly-Supervised Semantic Segmentation shjo-april/DHR 2024
3 FMA-WSSS (Swin-L) 81.6 Foundation Model Assisted Weakly Supervised Semantic Segmentation HAL-42/FMA-WSSS 2023
4 WeakTr (ViT-S, multi-stage) 79.0 WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation hustvl/weaktr 2023
5 MARS (ResNet-101, multi-stage) 77.2 MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic Segmentation shjo-april/mars 2023
6 WSSS-SAM(DeepLabV2-ResNet101) 77.1 An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation Problems weixuansun/wsss_sam 2023
7 QA-CLIMS 75.5 Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation cvi-szu/qa-clims 2024
8 ISIM (ResNeSt-200) 74.98 ISIM: Iterative Self-Improved Model for Weakly Supervised Segmentation cenkbircanoglu/isim 2022
9 FBR 74.9 Fine-grained Background Representation for Weakly Supervised Semantic Segmentation YininKorea/FBR 2024
10 HSC 74.5 Hierarchical Semantic Contrast for Weakly Supervised Semantic Segmentation Wu0409/HSC_WSSS 2023
11 T2MDiffusion(DeepLabV2-ResNet101) 74.2 From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models Big-Brother-Pikachu/Text2Mask 2023
12 WeakTr (DeiT-S, multi-stage) 74.1 WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation hustvl/weaktr 2023
13 CLIP-ES(DeepLabV2-ResNet101) 73.9 CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation linyq2117/clip-es 2022
14 RS+EPM (ResNet-101, multi-stage) 73.6 RecurSeed and EdgePredictMix: Pseudo-Label Refinement Learning for Weakly Supervised Semantic Segmentation across Single- and Multi-Stage Frameworks shjo-april/recurseed_and_edgepredictmix · cenkbircanoglu/isim 2022
15 PPC (w/ EPS) 73.5 Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast usr922/wseg 2021
15 BECO(DeepLabV3Plus+MiT-B2) 73.5 Boundary-Enhanced Co-Training for Weakly Supervised Semantic Segmentation ShenghaiRong/BECO 2023
17 L2G (ResNet101, DeepLab-LargeFOV) 73.0 L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation pengtaojiang/l2g 2022
18 ORANDNet 72.9 Precision matters: Precision-aware ensemble for weakly supervised semantic segmentation engineerJPark/ORANDNet 2024
19 RCA 72.8 Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation maeve07/rca 2022
20 SFC(ResNet-101) 72.5 SFC: Shared Feature Calibration in Weakly Supervised Semantic Segmentation barrett-python/sfc 2024
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