Search Results for author:
Found 30 papers, 9 papers with code
MemBench: Towards Real-world Evaluation of Memory-Augmented Dialogue Systems
To bridge the gap, we construct a novel benchmark covering various memory recalling paradigms based on cognitive science and psychology theory.
Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data
To alleviate this issue, we propose a Candidate Pseudolabel Learning method, termed CPL, to fine-tune VLMs with suitable candidate pseudolabels of unlabeled data in downstream tasks.
BDetCLIP: Multimodal Prompting Contrastive Test-Time Backdoor Detection
In this paper, we provide the first attempt at a computationally efficient backdoor detection method to defend against backdoored CLIP in the inference stage.
Debiased Sample Selection for Combating Noisy Labels
To address this limitation, we propose a noIse-Tolerant Expert Model (ITEM) for debiased learning in sample selection.
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation
The key idea is to first break the rote memories by network pruning to address memorization overfitting in the inner loop, and then the gradients of pruned sub-networks naturally form the high-quality augmentation of the meta-gradient to alleviate learner overfitting in the outer loop.
Fine-Grained Classification with Noisy Labels
To this end, we propose a novel framework called stochastic noise-tolerated supervised contrastive learning (SNSCL) that confronts label noise by encouraging distinguishable representation.
Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization
Sample selection is an effective strategy to mitigate the effect of label noise in robust learning.
Imputing Missing Observations with Time Sliced Synthetic Minority Oversampling Technique
We present a simple yet novel time series imputation technique with the goal of constructing an irregular time series that is uniform across every sample in a data set.
Learning to Rectify for Robust Learning with Noisy Labels
In this paper, we propose warped probabilistic inference (WarPI) to achieve adaptively rectifying the training procedure for the classification network within the meta-learning scenario.
RaP-Net: A Region-wise and Point-wise Weighting Network to Extract Robust Features for Indoor Localization
Experimental results show that the proposed RaP-Net trained with OpenLORIS-Location dataset achieves excellent performance in the feature matching task and significantly outperforms state-of-the-arts feature algorithms in indoor localization.
Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation
Furthermore, the regularization parameter is simultaneously estimated to automatically adjust contribution of the physical model and {the} learned prior to reconstruct the final HR HSI.
DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features
For visual SLAM algorithms, though the theoretical framework has been well established for most aspects, feature extraction and association is still empirically designed in most cases, and can be vulnerable in complex environments.
Loop Closure Detection
Simultaneous Localization and Mapping
LMVP: Video Predictor with Leaked Motion Information
We propose a Leaked Motion Video Predictor (LMVP) to predict future frames by capturing the spatial and temporal dependencies from given inputs.
DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments
It is one of the state-of-the-art SLAM systems in high-dynamic environments.
Robotics
Hu-Fu: Hardware and Software Collaborative Attack Framework against Neural Networks
So, in this paper, we propose a hardware-software collaborative attack framework to inject hidden neural network Trojans, which works as a back-door without requiring manipulating input images and is flexible for different scenarios.
InverseNet: Solving Inverse Problems with Splitting Networks
We propose a new method that uses deep learning techniques to solve the inverse problems.
A Fast Algorithm Based on a Sylvester-like Equation for LS Regression with GMRF Prior
This paper presents a fast approach for penalized least squares (LS) regression problems using a 2D Gaussian Markov random field (GMRF) prior.
An inner-loop free solution to inverse problems using deep neural networks
For matrix inversion in the second sub-problem, we learn a convolutional neural network to approximate the matrix inversion, i. e., the inverse mapping is learned by feeding the input through the learned forward network.
Detecting Changes Between Optical Images of Different Spatial and Spectral Resolutions: a Fusion-Based Approach
Change detection is one of the most challenging issues when analyzing remotely sensed images.
Robust Fusion of Multi-Band Images with Different Spatial and Spectral Resolutions for Change Detection
To alleviate these issues, classical change detection methods are applied after independent preprocessing steps (e. g., resampling) used to get the same spatial and spectral resolutions for the pair of observed images.
Unsupervised Nonlinear Spectral Unmixing based on a Multilinear Mixing Model
In the community of remote sensing, nonlinear mixing models have recently received particular attention in hyperspectral image processing.
R-FUSE: Robust Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation
This paper proposes a robust fast multi-band image fusion method to merge a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image.
Multi-Band Image Fusion Based on Spectral Unmixing
The non-negativity and sum-to-one constraints resulting from the intrinsic physical properties of the abundances are introduced as prior information to regularize this ill-posed problem.
Fast Single Image Super-Resolution
Specifically, an analytical solution can be obtained and implemented efficiently for the Gaussian prior or any other regularization that can be formulated into an $\ell_2$-regularized quadratic model, i. e., an $\ell_2$-$\ell_2$ optimization problem.
Fast Spectral Unmixing based on Dykstra's Alternating Projection
This paper presents a fast spectral unmixing algorithm based on Dykstra's alternating projection.
Hyperspectral pansharpening: a review
In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state of the art methods for multispectral pansharpening, which have been adapted for hyperspectral data.
Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation
This paper proposes a fast multi-band image fusion algorithm, which combines a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image.
Hyperspectral and Multispectral Image Fusion based on a Sparse Representation
This paper presents a variational based approach to fusing hyperspectral and multispectral images.
Physical Computing With No Clock to Implement the Gaussian Pyramid of SIFT Algorithm
In this paper, we propose an active circuit network to implement multi-scale Gaussian filter, which is also called Gaussian Pyramid in image preprocessing.
Bayesian Fusion of Multi-Band Images
In this paper, a Bayesian fusion technique for remotely sensed multi-band images is presented.
