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Test-Time Training (TTT) proposes to adapt a pre-trained network to changing data distributions on-the-fly.
Shannon, C.E.: A mathematical theory of communication. The Bell system technical journal 27
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Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In: Proc. ICML (2015)
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Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: Domain-Adversarial Training of Neural Networks. JMLR 17
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Armeni, I., Sax, S., Zamir, A.R., Savarese, S.: Joint 2d-3d-semantic data for indoor scene understanding (2017)
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Chang, A., Dai, A., Funkhouser, T., Halber, M., Niessner, M., Savva, M., Song, S., Zeng, A., Zhang, Y.: Matterport3D: Learning from RGB-D data in indoor environments. International Conference on 3D Vision (3DV) (2017)
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Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: Scannet: Richly-annotated 3d reconstructions of indoor scenes. Proc. CVPR (2017)
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Smith, L.N., Topin, N.: Super-convergence: Very fast training of residual networks using large learning rates (2017)
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Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., Gall, J.: SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. In: Proc. of the IEEE/CVF International Conf. on Computer Vision (ICCV) (2019)
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Choy, C., Gwak, J.Y., Savarese, S.: 4d spatio-temporal convnets: Minkowski convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)
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Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. Proceedings of the International Conference on Learning Representations (2019)
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Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: Proc. ICLR (2019)
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Qin, C., You, H., Wang, L., Kuo, C.C.J., Fu, Y.: PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation. In: NeurIPS (2019)
2019
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Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: Kpconv: Flexible and deformable convolution for point clouds. Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2019)
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Geyer, J., Kassahun, Y., Mahmudi, M., Ricou, X., Durgesh, R., Chung, A.S., Hauswald, L., Pham, V.H., Mühlegg, M., Dorn, S., Fernandez, T., Jänicke, M., Mirashi, S., Savani, C., Sturm, M., Vorobiov, O., Oelker, M., Garreis, S., Schuberth, P.: A2D2: audi autonomous driving dataset (2020), http://www.a2d2.audi/
2020
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Jaritz, M., Vu, T.H., de Charette, R., Wirbel, E., Pérez, P.: xMUDA: Cross-modal unsupervised domain adaptation for 3D semantic segmentation. In: Proc. CVPR (2020)
2020
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Liang, J., Hu, D., Feng, J.: Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. In: International conference on machine learning. pp. 6028–6039. PMLR (2020)
2020
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Saltori, C., Lathuilière, S., Sebe, N., Ricci, E., Galasso, F.: Sf-uda3d: Source-free unsupervised domain adaptation for lidar-based 3d object detection. In: Proc. i3dv (2020)
2020
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Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A., Hardt, M.: Test-time training with self-supervision for generalization under distribution shifts. In: International conference on machine learning. pp. 9229–9248. PMLR (2020)
2020
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Wang, D., Shelhamer, E., Liu, S., Olshausen, B., Darrell, T.: Tent: Fully Test-time Adaptation by Entropy Minimization. In: Proc. ICLR (2020)
2020
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Wang, Y., Chen, X., You, Y., Li, L.E., Hariharan, B., Campbell, M., Weinberger, K.Q., Chao, W.L.: Train in Germany, Test in The USA: Making 3D Object Detectors Generalize. In: Proc. CVPR (2020)
2020
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Fruhwirth-Reisinger, C., Opitz, M., Possegger, H., Bischof, H.: FAST3D: Flow-Aware Self-Training for 3D Object Detectors. In: Proc. BMVC (2021)
2021
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Iwasawa, Y., Matsuo, Y.: Test-time classifier adjustment module for model-agnostic domain generalization. Advances in Neural Information Processing Systems 34
2021
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Luo, Z., Cai, Z., Zhou, C., Zhang, G., Zhao, H., Yi, S., Lu, S., Li, H., Zhang, S., Liu, Z.: Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency. In: Proc. CVPR (2021)
2021
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Nekrasov, A., Schult, J., Litany, O., Leibe, B., Engelmann, F.: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. In: International Conference on 3D Vision (3DV) (2021)
2021
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Peng, D., Lei, Y., Li, W., Zhang, P., Guo, Y.: Sparse-to-dense feature matching: Intra and inter domain cross-modal learning in domain adaptation for 3d semantic segmentation. In: Proc. ICCV (2021)
2021
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Zhang, M., Levine, S., Finn, C.: Memo: Test time robustness via adaptation and augmentation. Advances in Neural Information Processing Systems 35
2022
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Cao, H., Xu, Y., Yang, J., Yin, P., Yuan, S., Xie, L.: Mopa: Multi-modal prior aided domain adaptation for 3d semantic segmentation (2023)
2023
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Darcet, T., Oquab, M., Mairal, J., Bojanowski, P.: Vision transformers need registers (2023)
2023
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Döbler, M., Marsden, R.A., Yang, B.: Robust mean teacher for continual and gradual test-time adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7704–7714 (2023)
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Hermosilla, P.: Point neighborhood embeddings (2023)
2023
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning (ICML) (2021)
2021
Cited alongside, same era.
Yang, J., Shi, S., Wang, Z., Li, H., Qi, X.: ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection. In: Proc. CVPR (2021)
2021
Cited alongside, same era.
Yi, L., Gong, B., Funkhouser, T.: Complete & label: A domain adaptation approach to semantic segmentation of lidar point clouds. In: Proc. CVPR (2021)
2021
Cited alongside, same era.
Zhao, H., Jiang, L., Jia, J., Torr, P.H., Koltun, V.: Point transformer. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2021)
2021
Cited alongside, same era.
Boudiaf, M., Mueller, R., Ben Ayed, I., Bertinetto, L.: Parameter-free online test-time adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8344–8353 (2022)
2022
Cited alongside, same era.
Chen, D., Wang, D., Darrell, T., Ebrahimi, S.: Contrastive test-time adaptation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (2022)
2022
Cited alongside, same era.
Gandelsman, Y., Sun, Y., Chen, X., Efros, A.: Test-time training with masked autoencoders. Advances in Neural Information Processing Systems 35
2022
Cited alongside, same era.
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 16000–16009 (2022)
2022
Cited alongside, same era.
2023
Later among the works it cites.
2023
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Lin, W., Mirza, M.J., Kozinski, M., Possegger, H., Kuehne, H., Bischof, H.: Video Test-Time Adaptation for Action Recognition. In: Proc. CVPR (2023)
2023
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Malić, D., Fruhwirth-Reisinger, C., Possegger, H., Bischof, H.: Sailor: Scaling anchors via insights into latent object representation. In: Proc. WACV (2023)
2023
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Mirza, M.J., Shin, I., Lin, W., Schriebl, A., Sun, K., Choe, J., Kozinski, M., Possegger, H., Kweon, I.S., Yoon, K.J., Bischof, H.: Mate: Masked autoencoders are online 3d test-time learners. Proc. ICCV (2023)
2023
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Mirza, M.J., Soneira, P.J., Lin, W., Kozinski, M., Possegger, H., Bischof, H.: ActMAD: Activation Matching to Align Distributions for Test-Time Training. In: Proc. CVPR (2023)
2023
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Niu, S., Wu, J., Zhang, Y., Wen, Z., Chen, Y., Zhao, P., Tan, M.: Towards stable test-time adaptation in dynamic wild world. In: Internetional Conference on Learning Representations (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Saltori, C., Galasso, F., Fiameni, G., Sebe, N., Poiesi, F., Ricci, E.: Compositional semantic mix for domain adaptation in point cloud segmentation. IEEE TPAMI (2023)
2023
Later among the works it cites.
Shaban, A., Lee, J., Jung, S., Meng, X., Boots, B.: Lidar-uda: Self-ensembling through time for unsupervised lidar domain adaptation. In: Proc. ICCV (2023)
2023
Later among the works it cites.
Wang, P.S.: Octformer: Octree-based transformers for 3d point clouds. ACM Transactions on Graphics (SIGGRAPH) (2023)
2023
Later among the works it cites.
Wu, X., Tian, Z., Wen, X., Peng, B., Liu, X., Yu, K., Zhao, H.: Towards large-scale 3d representation learning with multi-dataset point prompt training (2023)
2023
Later among the works it cites.
Xing, B., Ying, X., Wang, R., Yang, J., Chen, T.: Cross-modal contrastive learning for domain adaptation in 3d semantic segmentation. In: Proc. AAAI (2023)
2023
Later among the works it cites.
Yang, Y.Q., Guo, Y.X., Xiong, J.Y., Liu, Y., Pan, H., Wang, P.S., Tong, X., Guo, B.: Swin3d: A pretrained transformer backbone for 3d indoor scene understanding (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Wu, X., Jiang, L., Wang, P.S., Liu, Z., Liu, X., Qiao, Y., Ouyang, W., He, T., Zhao, H.: Point transformer v3: Simpler, faster, stronger. In: Proceedings of 28th IEEE Conference on Computer Vision and Pattern Recognition (2024)
2024
Closest in time.
Wu, X., Tian, Z., Wen, X., Peng, B., Liu, X., Yu, K., Zhao, H.: Towards large-scale 3d representation learning with multi-dataset point prompt training. In: Proceedings of 28th IEEE Conference on Computer Vision and Pattern Recognition (2024)
2024
Closest in time.