Weakly Supervised Object Localization Based on Implicit Spatial Constraints
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5597-4_35
Reference24 articles.
1. Bai, H., et al.: Weakly supervised object localization via transformer with implicit spatial calibration. In: European Conference on Computer Vision, pp. 612–628. Springer (2022)
2. Chen, Z., et al.: Lctr: on awakening the local continuity of transformer for weakly supervised object localization. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 410–418 (2022)
3. Choe, J., Shim, H.: Attention-based dropout layer for weakly supervised object localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2219–2228 (2019)
4. Gao, W., et al.: Ts-cam: token semantic coupled attention map for weakly supervised object localization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2886–2895 (2021)
5. Guo, G., et al.: Strengthen learning tolerance for weakly supervised object localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7403–7412 (2021)
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