no code implementations • 22 Feb 2024 • Renyi Mao, Qingshan Xu, Peng Zheng, Ye Wang, Tieru Wu, Rui Ma
In this paper, we aim for both fast and high-quality implicit field learning, and propose TaylorGrid, a novel implicit field representation which can be efficiently computed via direct Taylor expansion optimization on 2D or 3D grids.
1 code implementation • 9 Jan 2024 • Jiaxing He, Bingzhe Hou, Tieru Wu, Yue Xin
Two important problems in the field of Topological Data Analysis are defining practical multifiltrations on objects and showing ability of TDA to detect the geometry.
no code implementations • 29 Dec 2023 • Linlian Jiang, Pan Chen, Ye Wang, Tieru Wu, Rui Ma
Inferring missing regions from severely occluded point clouds is highly challenging.
no code implementations • 7 Dec 2023 • Shuangmei Wang, Yang Cao, Tieru Wu
Few-shot class-incremental learning (FSCIL) struggles to incrementally recognize novel classes from few examples without catastrophic forgetting of old classes or overfitting to new classes.
1 code implementation • 2 Jan 2023 • Shuangmei Wang, Rui Ma, Tieru Wu, Yang Cao
Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification.
no code implementations • 16 Nov 2022 • Yongjie Chen, Tieru Wu
Previous methods mainly utilize temporally adjacent frames to assist the reconstruction of target frames.
1 code implementation • 17 Jun 2022 • Hui Li, Zihao Li, Rui Ma, Tieru Wu
In this paper, we propose a novel CAM weighting scheme, named FD-CAM, to improve both the faithfulness and discriminability of the CAM-based CNN visual explanation.
no code implementations • 19 Dec 2018 • Chuangye Zhang, Yan Niu, Tieru Wu, Xi-Ming Li
Image warping is a necessary step in many multimedia applications such as texture mapping, image-based rendering, panorama stitching, image resizing and optical flow computation etc.