Page 475 - 《软件学报》2025年第9期
P. 475
4386 软件学报 2025 年第 36 卷第 9 期
Vancouver: IEEE, 2023. 15305–15314. [doi: 10.1109/CVPR52729.2023.01469]
[30] Chen ZZ, Sun QR. Extracting class activation maps from non-discriminative features as well. In: Proc. of the 2023 IEEE/CVF Conf. on
Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023. 3135–3144. [doi: 10.1109/CVPR52729.2023.00306]
[31] Xie JH, Hou XX, Ye K, Shen LL. CLIMS: Cross language image matching for weakly supervised semantic segmentation. In: Proc. of the
2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 4473–4482. [doi: 10.1109/CVPR52688.
2022.00444]
[32] Wu T, Huang JS, Gao GY, Wei XM, Wei XL, Luo X, Liu CH. Embedded discriminative attention mechanism for weakly supervised
semantic segmentation. In: Proc. of the 2021 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021.
16765–16774. [doi: 10.1109/CVPR46437.2021.01649]
[33] Lee J, Kim E, Yoon S. Anti-adversarially manipulated attributions for weakly and semi-supervised semantic segmentation. In: Proc. of
the 2021 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021. 4071–4080. [doi: 10.1109/CVPR46437.
2021.00406]
[34] Li Y, Kuang ZH, Liu LY, Chen YM, Zhang W. Pseudo-mask matters in weakly-supervised semantic segmentation. In: Proc. of the 2021
IEEE/CVF Int’l Conf. on Computer Vision. Montreal: IEEE, 2021. 6964–6973. [doi: 10.1109/ICCV48922.2021.00688]
[35] Jo S, Yu IJ, Kim K. MARS: Model-agnostic biased object removal without additional supervision for weakly-supervised semantic
segmentation. In: Proc. of the 2023 IEEE/CVF Int’l Conf. on Computer Vision. Paris: IEEE, 2023. 614–623. [doi: 10.1109/ICCV51070.
2023.00063]
[36] Lee S, Lee M, Lee J, Shim H. Railroad is not a train: Saliency as pseudo-pixel supervision for weakly supervised semantic segmentation.
In: Proc. of the 2021 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021. 5495–5505. [doi: 10.1109/
CVPR46437.2021.00545]
[37] Sun WX, Zhang J, Barnes N. Inferring the class conditional response map for weakly supervised semantic segmentation. In: Proc. of the
2022 IEEE/CVF Winter Conf. on Applications of Computer Vision. Waikoloa: IEEE, 2022. 2653–2662. [doi: 10.1109/WACV51458.
2022.00271]
[38] Zhou TF, Zhang MJ, Zhao F, Li JW. Regional semantic contrast and aggregation for weakly supervised semantic segmentation. In: Proc.
of the 2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 4299–4309. [doi: 10.1109/
CVPR52688.2022.00426]
[39] Su YK, Sun RZ, Lin GS, Wu QY. Context decoupling augmentation for weakly supervised semantic segmentation. In: Proc. of the 2021
IEEE/CVF Int’l Conf. on Computer Vision. Montreal: IEEE, 2021. 7004–7014. [doi: 10.1109/ICCV48922.2021.00692]
[40] Li Y, Duan YQ, Kuang ZH, Chen YM, Zhang W, Li XM. Uncertainty estimation via response scaling for pseudo-mask noise mitigation
in weakly-supervised semantic segmentation. In: Proc. of the 36th AAAI Conf. on Artificial Intelligence. AAAI Press, 2022. 1447–1455.
[doi: 10.1609/aaai.v36i2.20034]
[41] Chen ZZ, Wang T, Wu XW, Hua XS, Zhang HW, Sun QR. Class re-activation maps for weakly-supervised semantic segmentation. In:
Proc. of the 2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 959–968. [doi: 10.1109/
CVPR52688.2022.00104]
[42] Lee M, Kim D, Shim H. Threshold matters in WSSS: Manipulating the activation for the robust and accurate segmentation model against
thresholds. In: Proc. of the 2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 4330–4339.
[doi: 10.1109/CVPR52688.2022.00429]
[43] Lee J, Oh SJ, Yun S, Choe J, Kim E, Yoon S. Weakly supervised semantic segmentation using out-of-distribution data. In: Proc. of the
2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 16897–16906. [doi: 10.1109/
CVPR52688.2022.01639]
[44] Chen Q, Yang LX, Lai JH, Xie XH. Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation.
In: Proc. of the 2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 4288–4298. [doi: 10.
1109/CVPR52688.2022.00425]
[45] Rossetti S, Zappia D, Sanzari M, Schaerf M, Pirri F. Max pooling with vision Transformers reconciles class and shape in weakly
supervised semantic segmentation. In: Proc. of the 17th European Conf. on Computer Vision. Tel Aviv: Springer, 2022. 446–463. [doi: 10.
1007/978-3-031-20056-4_26]
[46] Yoon SH, Kweon H, Cho J, Kim S, Yoon KJ. Adversarial erasing framework via triplet with gated pyramid pooling layer for weakly
supervised semantic segmentation. In: Proc. of the 17th European Conf. on Computer Vision. Tel Aviv: Springer, 2022. 326–344. [doi: 10.
1007/978-3-031-19818-2_19]
[47] Kweon H, Yoon SH, Yoon KJ. Weakly supervised semantic segmentation via adversarial learning of classifier and reconstructor. In:
Proc. of the 2023 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023. 11329–11339. [doi: 10.1109/
CVPR52729.2023.01090]
[48] Rong SH, Tu BH, Wang ZL, Li JJ. Boundary-enhanced co-training for weakly supervised semantic segmentation. In: Proc. of the 2023

