Search Results for author:
Found 16 papers, 2 papers with code
$\mathrm{F^2Depth}$: Self-supervised Indoor Monocular Depth Estimation via Optical Flow Consistency and Feature Map Synthesis
To evaluate the generalization ability of our $\mathrm{F^2Depth}$, we collect a Campus Indoor depth dataset composed of approximately 1500 points selected from 99 images in 18 scenes.
Indoor Monocular Depth Estimation
Monocular Depth Estimation
+2
Feature Denoising Diffusion Model for Blind Image Quality Assessment
Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks.
Projective Parallel Single-Pixel Imaging: 3D Structured Light Scanning Under Global Illumination
The 4D LTC in pPSI are reduced to projection functions, thereby enabling a highly efficient data capture process.
Adaptive Feature Selection for No-Reference Image Quality Assessment using Contrastive Mitigating Semantic Noise Sensitivity
The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically use feature extraction in upstream backbone networks, which assumes that all extracted features are relevant.
Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment
Concretely, by innovatively introducing a novel feature distillation method in IQA, we propose a new framework to learn comparative knowledge from non-aligned reference images.
Data-Driven Minimax Optimization with Expectation Constraints
We propose a class of efficient primal-dual algorithms to tackle the minimax expectation-constrained problem, and show that our algorithms converge at the optimal rate of $\mathcal{O}(\frac{1}{\sqrt{N}})$.
Robust Security Analysis Based on Random Geometry Theory for Satellite-Terrestrial-Vehicle Network
Driven by B5G and 6G technologies, multi-network fusion is an indispensable tendency for future communications.
On Geometric Connections of Embedded and Quotient Geometries in Riemannian Fixed-rank Matrix Optimization
By applying the general procedure to the fixed-rank positive semidefinite (PSD) and general matrix optimization, we establish an exact Riemannian gradient connection under two geometries at every point on the manifold and sandwich inequalities between the spectra of Riemannian Hessians at Riemannian first-order stationary points (FOSPs).
Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization
In this paper, we consider the geometric landscape connection of the widely studied manifold and factorization formulations in low-rank positive semidefinite (PSD) and general matrix optimization.
Few-Shot Meta-Learning on Point Cloud for Semantic Segmentation
With a good understanding of environmental information, construction robots can work better.
Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence
In this paper, we propose {\it \underline{R}ecursive} {\it \underline{I}mportance} {\it \underline{S}ketching} algorithm for {\it \underline{R}ank} constrained least squares {\it \underline{O}ptimization} (RISRO).
Neural Architecture Search For Fault Diagnosis
In this paper, we proposed a NAS method for fault diagnosis using reinforcement learning.
MixPath: A Unified Approach for One-shot Neural Architecture Search
However, current two-stage neural architecture search methods are mainly limited to single-path search spaces.
SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search
To discover powerful yet compact models is an important goal of neural architecture search.
Ranked #76 on
Neural Architecture Search
on ImageNet
Learning Markov models via low-rank optimization
We show that one can estimate the full transition model accurately using a trajectory of length that is proportional to the total number of states.
Estimation of Markov Chain via Rank-Constrained Likelihood
This paper studies the estimation of low-rank Markov chains from empirical trajectories.
