Clip-cluster: Clip-guided attribute hallucination for face clustering
One of the most important yet rarely studied challenges for supervised face clustering is the
large intra-class variance caused by different face attributes such as age, pose, and …
large intra-class variance caused by different face attributes such as age, pose, and …
Hireview: Hierarchical taxonomy-driven automatic literature review generation
In this work, we present HiReview, a novel framework for hierarchical taxonomy-driven
automatic literature review generation. With the exponential growth of academic documents …
automatic literature review generation. With the exponential growth of academic documents …
Hierarchical graph pattern understanding for zero-shot video object segmentation
The optical flow guidance strategy is ideal for obtaining motion information of objects in the
video. It is widely utilized in video segmentation tasks. However, existing optical flow-based …
video. It is widely utilized in video segmentation tasks. However, existing optical flow-based …
DCOM-GNN: A deep clustering optimization method for graph neural networks
H Yang, J Wang, R Duan, C Yan - Knowledge-Based Systems, 2023 - Elsevier
Deep clustering plays an important role in data analysis, and with the prevalence of graph
data nowadays, various deep clustering models on graph are constantly proposed …
data nowadays, various deep clustering models on graph are constantly proposed …
Deep Incomplete Multi-view Clustering via Multi-level Imputation and Contrastive Alignment
Deep incomplete multi-view clustering (DIMVC) aims to enhance clustering performance by
capturing consistent information from incomplete multiple views using deep models. Most …
capturing consistent information from incomplete multiple views using deep models. Most …
VertexSerum: Poisoning Graph Neural Networks for Link Inference
Graph neural networks (GNNs) have brought superb performance to various applications
utilizing graph structural data, such as social analysis and fraud detection. The graph links …
utilizing graph structural data, such as social analysis and fraud detection. The graph links …
HG-CAD: hierarchical graph learning for material prediction and recommendation in computer-aided design
To support intelligent computer-aided design (CAD), we introduce a machine learning
architecture, namely HG-CAD, that recommends assembly body material through joint …
architecture, namely HG-CAD, that recommends assembly body material through joint …
Equivariant graph hierarchy-based neural networks
Abstract Equivariant Graph neural Networks (EGNs) are powerful in characterizing the
dynamics of multi-body physical systems. Existing EGNs conduct flat message passing …
dynamics of multi-body physical systems. Existing EGNs conduct flat message passing …
Neural trees for learning on graphs
Abstract Graph Neural Networks (GNNs) have emerged as a flexible and powerful approach
for learning over graphs. Despite this success, existing GNNs are constrained by their local …
for learning over graphs. Despite this success, existing GNNs are constrained by their local …
Comprehensive relationship reasoning for composed query based image retrieval
Composed Query Based Image Retrieval (CQBIR) aims at searching images relevant to a
composed query, ie, a reference image together with a modifier text. Compared with …
composed query, ie, a reference image together with a modifier text. Compared with …