In this paper, we propose a generic Multi-modal Multi-view Semantic Embedding (MMSE) framework via a Bayesian model for question answering. Compared with existing semantic learning methods, the proposed model m
Multi-view image classification with visual, semantic and view consistency. IEEE Transactions on Image Processing, 2020, 29: 617–627 Article MathSciNet Google Scholar Xu C, Tao D, Xu C. A survey on multi-view learning. 2013, arXiv preprint arXiv: 1304.5634 Luo S, Zhang C, Zhang W, ...
Frome, A., et al.: Devise: a deep visual-semantic embedding model. In: NIPS 2013, pp. 2121–2129 (2013) Google Scholar Bronstein, M., Michel, F., Paragios, N.: Data fusion through cross-modality metric learning using similarity-sensitive hashing. In: CVPR 2010, pp. 3594–3601 (201...
work related to the heterogeneous network embedding technique is introduced; “Preliminary” section describes some preliminaries; “Multi-view heterogeneous graph contrastive learning” section describes the implementation of the multi-view contrastive learning for heterogeneous network embedding; “Multi-view ...
CAPE: Camera View Position Embedding for Multi-View 3D Object Detection Kaixin Xiong*,1, Shi Gong∗,2, Xiaoqing Ye∗,2, Xiao Tan2, Ji Wan2, Errui Ding2, Jingdong Wang†,2, Xiang Bai1 1Huazhong University of Science and Technology, 2Baidu Inc...
“Grad-Cam: Visual Explanations from Deep Networks via Gradient-Based Localization.” Paper presented at the Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 618–626. (Open in a new window)Google Scholar Shen, T., Y. Wei, L. Kang, S. Wan, and Y. H...
A three-view embedding approach was proposed in [10] to fuse visual content, tags, and semantic information. However, it is difficult for these methods to effectively capture the non-linear relation between image and text. Meanwhile, as pointed out in [12], CCA is hard to scale up to ...
4, the propagation transformer con- sists of three main components: (1) the motion-aware layer normalization module implicitly updates the object state ac- cording to the context embedding and motion information recorded in the memory queue; (2) the...
After entering the fine-tuning stage, we first initially enhance the commonality between independent specific views through the transformer layer, and then further strengthen these commonalities through contrastive learning on the semantic labels of each view, so as to obtain more accurate clustering ...
and generation tasks that involve 3D visual data. In addition, the different deep networks that have been suggested for 3D vision understanding are summarized. Also, the intersection between this field and neuroscience and psychology has been explained to understand the human visual system better. The...