Search Results for author:
Found 17 papers, 6 papers with code
MCA: Moment Channel Attention Networks
In this paper, we investigate the statistical moments of feature maps within a neural network.
DeepBranchTracer: A Generally-Applicable Approach to Curvilinear Structure Reconstruction Using Multi-Feature Learning
Curvilinear structures, which include line-like continuous objects, are fundamental geometrical elements in image-based applications.
Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark
Recently significant progress has been made in human action recognition and behavior prediction using deep learning techniques, leading to improved vision-based semantic understanding.
Latent Processes Identification From Multi-View Time Series
Understanding the dynamics of time series data typically requires identifying the unique latent factors for data generation, \textit{a. k. a.
Discriminative Radial Domain Adaptation
Domain adaptation methods reduce domain shift typically by learning domain-invariant features.
iDAG: Invariant DAG Searching for Domain Generalization
In this work, we first characterize that this failure of conventional ML models in DG is attributed to an inadequate identification of causal structures.
SIGMA: A Structural Inconsistency Reducing Graph Matching Algorithm
In this paper, we propose a novel criterion to measure the graph matching accuracy, structural inconsistency (SI), which is defined based on the network topological structure.
Approximating Optimal Transport via Low-rank and Sparse Factorization
Optimal transport (OT) naturally arises in a wide range of machine learning applications but may often become the computational bottleneck.
Gaussian Context Transformer
These approaches commonly learn the relationship between global contexts and attention activations by using fully-connected layers or linear transformations.
Partial Gromov-Wasserstein Learning for Partial Graph Matching
Graph matching finds the correspondence of nodes across two graphs and is a basic task in graph-based machine learning.
Interventional Domain Adaptation
To address this issue, we intervene in the learning of feature discriminability using unlabeled target data to guide it to get rid of the domain-specific part and be safely transferable.
Parameter-Efficient Person Re-identification in the 3D Space
To our knowledge, we are among the first attempts to conduct person re-identification in the 3D space.
A Decentralized Proximal Point-type Method for Saddle Point Problems
In this paper, we focus on solving a class of constrained non-convex non-concave saddle point problems in a decentralized manner by a group of nodes in a network.
Linear Context Transform Block
In this work, we first revisit the SE block, and then present a detailed empirical study of the relationship between global context and attention distribution, based on which we propose a simple yet effective module, called Linear Context Transform (LCT) block.
Bayesian Uncertainty Matching for Unsupervised Domain Adaptation
By imposing distribution matching on both features and labels (via uncertainty), label distribution mismatching in source and target data is effectively alleviated, encouraging the classifier to produce consistent predictions across domains.
Exploiting Local Feature Patterns for Unsupervised Domain Adaptation
In this paper, we present a method for learning domain-invariant local feature patterns and jointly aligning holistic and local feature statistics.
Efficient Spiking Neural Networks with Logarithmic Temporal Coding
A Spiking Neural Network (SNN) can be trained indirectly by first training an Artificial Neural Network (ANN) with the conventional backpropagation algorithm, then converting it into an SNN.
