Search Results for author:
Found 8 papers, 6 papers with code
What is Learned in Deep Uncalibrated Photometric Stereo?
Based on this insight, we propose a guided calibration network, named GCNet, that explicitly leverages object shape and shading information for improved lighting estimation.
Res-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning
Our findings elucidate that our proposed methodology establishes a new benchmark for SOTA performance in food recognition on the CNFOOD-241 dataset.
Ranked #1 on
Fine-Grained Image Recognition
on CNFOOD-241-Chen
Deep Photometric Stereo for Non-Lambertian Surfaces
To deal with the uncalibrated scenario where light directions are unknown, we introduce a new convolutional network, named LCNet, to estimate light directions from input images.
ET-USB: Transformer-Based Sequential Behavior Modeling for Inbound Customer Service
Therefore, we created a new approach, ET-USB, that incorporates users' sequential and nonsequential features; we apply the powerful Transformer encoder, a self-attention network model, to capture the information underlying user behavior sequences.
Learning Transparent Object Matting
In this paper, we formulate transparent object matting as a refractive flow estimation problem, and propose a deep learning framework, called TOM-Net, for learning the refractive flow.
Self-calibrating Deep Photometric Stereo Networks
This paper proposes an uncalibrated photometric stereo method for non-Lambertian scenes based on deep learning.
PS-FCN: A Flexible Learning Framework for Photometric Stereo
This paper addresses the problem of photometric stereo for non-Lambertian surfaces.
TOM-Net: Learning Transparent Object Matting from a Single Image
In this paper, we first formulate transparent object matting as a refractive flow estimation problem.
