Gradual adaption with memory mechanism for image-based 3D model retrieval

Zhou, 2019, Dual-level embedding alignment network for 2d image-based 3d object retrieval, 1667 Ren, 2021, A comprehensive survey of neural architecture search: challenges and solutions, ACM Comput. Surv., 54, 1, 10.1145/3447582 Hang, 2015, Multi-view convolutional neural networks for 3d shape recognition, 945 Qi, 2017, Pointnet: Deep learning on point sets for 3d classification and segmentation, 652 Zhou, 2020, Semantic consistency guided instance feature alignment for 2d image-based 3d shape retrieval, 925 Li, 2018, Dynamic affinity graph construction for spectral clustering using multiple features, IEEE Trans. Neural Netw. Learn. Syst., 29, 6323, 10.1109/TNNLS.2018.2829867 Chang, 2015, Compound rank-k projections for bilinear analysis, IEEE Trans. Neural Netw. Learn. Syst., 27, 1502, 10.1109/TNNLS.2015.2441735 2016, Pointnet: A 3d convolutional neural network for real-time object class recognition, 1578 Qi, 2017, Pointnet++: Deep hierarchical feature learning on point sets in a metric space, 5099 Maturana, 2015, Voxnet: A 3d convolutional neural network for real-time object recognition, 922 Zhirong, 2015, 3d shapenets: A deep representation for volumetric shapes, 1912 Sedaghat, 2016 Kim, 2020, Category-specific upright orientation estimation for 3d model classification and retrieval, Image Vis. Comput., 96, 10.1016/j.imavis.2020.103900 Nie, 2021, Dan: deep-attention network for 3d shape recognition, IEEE Trans. Image Process., 30, 4371, 10.1109/TIP.2021.3071687 Bai, 2017, Gift: Towards scalable 3d shape retrieval, IEEE Trans. Multimedia, 19, 1257, 10.1109/TMM.2017.2652071 Grabner, 2018, 3d pose estimation and 3d model retrieval for objects in the wild, 3022 He, 2018, Triplet-center loss for multi-view 3d object retrieval, 1945 Li, 2019, Angular triplet-center loss for multi-view 3d shape retrieval, 33, 8682 Xie, 2017, Learning barycentric representations of 3d shapes for sketch-based 3d shape retrieval, 5068 Nie, 2020, Deep correlated joint network for 2-d image-based 3-d model retrieval, IEEE Trans. Cybern., 52, 1862, 10.1109/TCYB.2020.2995415 2018, Gvcnn: Group-view convolutional neural networks for 3d shape recognition, 264 Wang, 2015, Sketch-based 3d shape retrieval using convolutional neural networks, 1875 Zhu, 2016, Learning cross-domain neural networks for sketch-based 3d shape retrieval, 30 Dai, 2017, Deep correlated metric learning for sketch-based 3d shape retrieval Pan-pan, 2018, Image-based 3d model retrieval using manifold learning, Front. Inform. Technol. Electron. Eng., 19, 1397, 10.1631/FITEE.1601764 Zhou, 2021, Hierarchical instance feature alignment for 2d image-based 3d shape retrieval, 839 Grabner, 2019, Location field descriptors: Single image 3d model retrieval in the wild, 583 Caron, 2018, Deep clustering for unsupervised learning of visual features, 132 2020, Unsupervised learning of visual features by contrasting cluster assignments Belal, 2021, Knowledge distillation methods for efficient unsupervised adaptation across multiple domains, Image Vis. Comput., 108 Zhou, 2021, Cluster adaptation networks for unsupervised domain adaptation - sciencedirect, Image Vis. Comput., 108, 10.1016/j.imavis.2021.104137 Wang, 2020, Exploiting global camera network constraints for unsupervised video person re-identification Liu, 2021, Combining graph neural networks with expert knowledge for smart contract vulnerability detection Berthelot, 2019, Mixmatch: A holistic approach to semi-supervised learning, Adv. Neural Inf. Proces. Syst., 32 2019, Semi-supervised learning with graph learning-convolutional networks, 11313 Yue, 2017, Semi-supervised learning through adaptive laplacian graph trimming, Image Vis. Comput., 60, 38, 10.1016/j.imavis.2016.11.013 Wang, 2021, Learning person re-identification models from videos with weak supervision, IEEE Trans. Image Process., 30, 3017, 10.1109/TIP.2021.3056223 Sun, 2016, Return of frustratingly easy domain adaptation, 30 Pan, 2010, Domain adaptation via transfer component analysis, IEEE Trans. Neural Netw., 22, 199, 10.1109/TNN.2010.2091281 Tzeng, 2014 Sun, 2016, Deep coral: Correlation alignment for deep domain adaptation, 443 Ganin, 2016, Domain-adversarial training of neural networks, J. Mach. Learn. Res., 17 Tzeng, 2017, Adversarial discriminative domain adaptation, 7167 Long, 2018, Conditional adversarial domain adaptation, 1640 2020, Gradually vanishing bridge for adversarial domain adaptation, 12455 Snell, 2017, Prototypical networks for few-shot learning, 4080 Ganin, 2015, Unsupervised domain adaptation by backpropagation, 1180 2019, Shrec 2019-monocular image based 3d model retrieval, 1 He, 2016, Deep residual learning for image recognition, 770 2018, Visual domain adaptation with manifold embedded distribution alignment, 402 Zhang, 2017, Joint geometrical and statistical alignment for visual domain adaptation, 1859 2017, Deep transfer learning with joint adaptation networks, 2208 Simonyan, 2014 Van der Maaten, 2008, Visualizing data using t-sne, J. Mach. Learn. Res., 9 Ma, 2019, Paddlepaddle: an open-source deep learning platform from industrial practice, Front. Data Domputing, 1, 105 PaddlePaddle