8 citations · 12 across the 7 of their papers we have counts for
8 papers
Exposure Bias Can Alleviate Itself via Directional and Frequency Rectification in Flow Matching
Guanbo Huang, Jingjia Mao, Fanding Huang +9
Flow Matching (FM) has achieved remarkable generative performance, yet it suffers from exposure bias due to discrepancies between training and inference. Existing mitigation strate…
Spatially and Spectrally Consistent Deep Functional Maps
Mingze Sun, Shiwei Mao, Puhua Jiang +2
Cycle consistency has long been exploited as a powerful prior for jointly optimizing maps within a collection of shapes. In this paper, we investigate its utility in the approaches…
Neural Intrinsic Embedding for Non-rigid Point Cloud Matching
Puhua Jiang, Mingze Sun, Ruqi Huang
As a primitive 3D data representation, point clouds are prevailing in 3D sensing, yet short of intrinsic structural information of the underlying objects. Such discrepancy poses gr…
Optical Neural Ordinary Differential Equations
Yun Zhao, Hang Chen, Min Lin +5
Increasing the layer number of on-chip photonic neural networks (PNNs) is essential to improve its model performance. However, the successively cascading of network hidden layers r…
Cross-Camera Deep Colorization
Yaping Zhao, Haitian Zheng, Mengqi Ji +1
In this paper, we consider the color-plus-mono dual-camera system and propose an end-to-end convolutional neural network to align and fuse images from it in an efficient and cost-e…
EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation
Yaping Zhao, Mengqi Ji, Ruqi Huang +2
In this paper, we consider the problem of reference-based video super-resolution(RefVSR), i.e., how to utilize a high-resolution (HR) reference frame to super-resolve a low-resolut…
OperatorNet: Recovering 3D Shapes From Difference Operators
Ruqi Huang, Marie-Julie Rakotosaona, Panos Achlioptas +2
This paper proposes a learning-based framework for reconstructing 3D shapes from functional operators, compactly encoded as small-sized matrices. To this end we introduce a novel n…
Latent Space Representation for Shape Analysis and Learning
Ruqi Huang, Panos Achlioptas, Leonidas Guibas +1
We propose a novel shape representation useful for analyzing and processing shape collections, as well for a variety of learning and inference tasks. Unlike most approaches that ca…