Search Results for author:
Found 14 papers, 3 papers with code
Heterogeneous Information Network Embedding for Meta Path based Proximity
A network embedding is a representation of a large graph in a low-dimensional space, where vertices are modeled as vectors.
Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks
Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks, where a meta-graph is a composition of meta-paths that captures the complex structural information.
Quantum droplets in two-dimensional optical lattices
We study the stability of zero-vorticity and vortex lattice quantum droplets (LQDs), which are described by a two-dimensional (2D) Gross-Pitaevskii (GP) equation with a periodic potential and Lee-Huang-Yang (LHY) term.
Pattern Formation and Solitons Quantum Physics
Adaptive Domain-Specific Normalization for Generalizable Person Re-Identification
It describes unseen target domain as a combination of the known source ones, and explicitly learns domain-specific representation with target distribution to improve the model's generalization by a meta-learning pipeline.
Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation
In this paper, to address more practical scenarios, we propose a new task, Lifelong Unsupervised Domain Adaptive (LUDA) person ReID.
Domain Adaptive Person Re-Identification
Knowledge Distillation
+4
Modality-Adaptive Mixup and Invariant Decomposition for RGB-Infrared Person Re-Identification
RGB-infrared person re-identification is an emerging cross-modality re-identification task, which is very challenging due to significant modality discrepancy between RGB and infrared images.
Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-Identification
In this paper, we propose a novel Debiased Batch Normalization via Gaussian Process approach (GDNorm) for generalizable person re-identification, which models the feature statistic estimation from BN layers as a dynamically self-refining Gaussian process to alleviate the bias to unseen domain for improving the generalization.
Generalizable Person Re-identification
Representation Learning
Deep Frequency Filtering for Domain Generalization
Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge.
The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks
The stochastic block model (SBM) is one of the most widely used generative models for network data.
A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networks
Networks and temporal point processes serve as fundamental building blocks for modeling complex dynamic relational data in various domains.
A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation
We propose to model HLA compatibility using a network, where the nodes denote different HLAs of the donor and recipient, and edge weights denote compatibilities of the HLAs, which can be positive or negative.
Adaptive Frequency Filters As Efficient Global Token Mixers
With this insight, we propose Adaptive Frequency Filtering (AFF) token mixer.
RelationVLM: Making Large Vision-Language Models Understand Visual Relations
The development of Large Vision-Language Models (LVLMs) is striving to catch up with the success of Large Language Models (LLMs), yet it faces more challenges to be resolved.
VisualCritic: Making LMMs Perceive Visual Quality Like Humans
In this paper, we explore this question and provide the answer "Yes!".
