Search Results for author:
Found 31 papers, 14 papers with code
GmNet: Revisiting Gating Mechanisms From A Frequency View
Gating mechanisms have emerged as an effective strategy integrated into model designs beyond recurrent neural networks for addressing long-range dependency problems.
PARF-Net: integrating pixel-wise adaptive receptive fields into hybrid Transformer-CNN network for medical image segmentation
However, existing hybrid methods still suffer deficient learning of local semantic features due to the fixed receptive fields of convolutions, and also fall short in effectively integrating local and long-range dependencies.
A Backdoor Attack Scheme with Invisible Triggers Based on Model Architecture Modification
Machine learning systems are vulnerable to backdoor attacks, where attackers manipulate model behavior through data tampering or architectural modifications.
Slicing Vision Transformer for Flexible Inference
Thus, we propose a general framework, named Scala, to enable a single network to represent multiple smaller ViTs with flexible inference capability, which aligns with the inherent design of ViT to vary from widths.
Accessing Vision Foundation Models at ImageNet-level Costs
Vision foundation models are renowned for their generalization ability due to massive training data.
Rewrite the Stars
Recent studies have drawn attention to the untapped potential of the "star operation" (element-wise multiplication) in network design.
Efficient Modulation for Vision Networks
We demonstrate that the modulation mechanism is particularly well suited for efficient networks and further tailor the modulation design by proposing the efficient modulation (EfficientMod) block, which is considered the essential building block for our networks.
Enhancing Building Energy Efficiency through Advanced Sizing and Dispatch Methods for Energy Storage
The solution is determined considering both capital costs in optimal sizing and operational benefits in optimal dispatch.
Quantum-Based Feature Selection for Multi-classification Problem in Complex Systems with Edge Computing
The complex systems with edge computing require a huge amount of multi-feature data to extract appropriate insights for their decision making, so it is important to find a feasible feature selection method to improve the computational efficiency and save the resource consumption.
BEV-DG: Cross-Modal Learning under Bird's-Eye View for Domain Generalization of 3D Semantic Segmentation
In light of this, we propose cross-modal learning under bird's-eye view for Domain Generalization (DG) of 3D semantic segmentation, called BEV-DG.
Image as Set of Points
Context clusters (CoCs) view an image as a set of unorganized points and extract features via simplified clustering algorithm.
Making Reconstruction-based Method Great Again for Video Anomaly Detection
With the same perturbation magnitude, the testing reconstruction error of the normal frames lowers more than that of the abnormal frames, which contributes to mitigating the overfitting problem of reconstruction.
Efficient Converted Spiking Neural Network for 3D and 2D Classification
Spiking Neural Networks (SNNs) have attracted enormous research interest due to their low-power and biologically plausible nature.
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective
The experimental evaluations manifest the superiority of SAT over other state-of-the-art AT mechanisms in defending against adversarial attacks against both output and intermediate layers.
A Close Look at Spatial Modeling: From Attention to Convolution
Vision Transformers have shown great promise recently for many vision tasks due to the insightful architecture design and attention mechanism.
Parameter-Efficient Masking Networks
We validate the potential of PEMN learning masks on random weights with limited unique values and test its effectiveness for a new compression paradigm based on different network architectures.
Label-Efficient Domain Generalization via Collaborative Exploration and Generalization
To escape from the dilemma between domain generalization and annotation costs, in this paper, we introduce a novel task named label-efficient domain generalization (LEDG) to enable model generalization with label-limited source domains.
A Fast Alternating Minimization Algorithm for Coded Aperture Snapshot Spectral Imaging Based on Sparsity and Deep Image Priors
Coded aperture snapshot spectral imaging (CASSI) is a technique used to reconstruct three-dimensional hyperspectral images (HSIs) from one or several two-dimensional projection measurements.
Towards Layer-wise Image Vectorization
Image rasterization is a mature technique in computer graphics, while image vectorization, the reverse path of rasterization, remains a major challenge.
Predicting Peak Day and Peak Hour of Electricity Demand with Ensemble Machine Learning
In this study, we develop a supervised machine learning approach to generate 1) the probability of the next operation day containing the peak hour of the month and 2) the probability of an hour to be the peak hour of the day.
Attention-based Cross-Layer Domain Alignment for Unsupervised Domain Adaptation
To further boost model adaptation performance, we propose a novel method called Attention-based Cross-layer Domain Alignment (ACDA), which captures the semantic relationship between the source and target domains across model layers and calibrates each level of semantic information automatically through a dynamic attention mechanism.
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework
We notice that detailed local geometrical information probably is not the key to point cloud analysis -- we introduce a pure residual MLP network, called PointMLP, which integrates no sophisticated local geometrical extractors but still performs very competitively.
Ranked #4 on
Point Cloud Segmentation
on PointCloud-C
Point Cloud Segmentation
Supervised Only 3D Point Cloud Classification
Online Self-Evolving Anomaly Detection in Cloud Computing Environments
As a distinct advantage of our framework, cloud system administrators only need to check a small number of detected anomalies, and their decisions are leveraged to update the detector.
Collaborative Semantic Aggregation and Calibration for Federated Domain Generalization
Domain generalization (DG) aims to learn from multiple known source domains a model that can generalize well to unknown target domains.
Instrumental Variable-Driven Domain Generalization with Unobserved Confounders
Meanwhile, the existing of unobserved confounders which affect the input features and labels simultaneously cause spurious correlation and hinder the learning of the invariant relationship contained in the conditional distribution.
Domain-Specific Bias Filtering for Single Labeled Domain Generalization
In this paper, we investigate a Single Labeled Domain Generalization (SLDG) task with only one source domain being labeled, which is more practical and challenging than the CDG task.
Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach
In real-world search, recommendation, and advertising systems, the multi-stage ranking architecture is commonly adopted.
Compressive Circular Polarization Snapshot Spectral Imaging
A compressive sensing based circular polarization snapshot spectral imaging system is proposed in this paper to acquire two-dimensional spatial, one-dimensional circular polarization (the right and left circular polarization), and one-dimensional spectral information, simultaneously.
Compressive Sensing
Optics
Image and Video Processing
78A10 (Primary)
I.4.2; I.4.4; I.4.5
Compressive spectral image classification using 3D coded convolutional neural network
The accuracy of classification is effectively improved by exploiting the synergy between the deep learning network and coded apertures.
Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine
Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation.
DCANet: Learning Connected Attentions for Convolutional Neural Networks
With DCANet, all attention blocks in a CNN model are trained jointly, which improves the ability of attention learning.
