84 citations · 144 across the 13 of their papers we have counts for
13 papers
VEML: A Plug-and-play Framework for Fusing Text and Structure Knowledge on Sparse Knowledge Graph Completion
Tao He, Ming Liu, Yixin Cao +5
Knowledge Graph Completion (KGC) aims to reason over known facts and infer missing links but achieves weak performances on those sparse Knowledge Graphs (KGs). Recent works introdu…
Towards Open-vocabulary Scene Graph Generation with Prompt-based Finetuning
Tao He, Lianli Gao, Jingkuan Song +1
Scene graph generation (SGG) is a fundamental task aimed at detecting visual relations between objects in an image. The prevailing SGG methods require all object classes to be give…
Semantic Compositional Learning for Low-shot Scene Graph Generation
Tao He, Lianli Gao, Jingkuan Song +2
Scene graphs provide valuable information to many downstream tasks. Many scene graph generation (SGG) models solely use the limited annotated relation triples for training, leading…
Exploiting Scene Graphs for Human-Object Interaction Detection
Tao He, Lianli Gao, Jingkuan Song +1
Human-Object Interaction (HOI) detection is a fundamental visual task aiming at localizing and recognizing interactions between humans and objects. Existing works focus on the visu…
A Computer-Aided Diagnosis System for Breast Pathology: A Deep Learning Approach with Model Interpretability from Pathological Perspective
Wei-Wen Hsu, Yongfang Wu, Chang Hao +6
Objective: We develop a computer-aided diagnosis (CAD) system using deep learning approaches for lesion detection and classification on whole-slide images (WSIs) with breast cancer…
Unsupervised Domain-adaptive Hash for Networks
Tao He, Lianli Gao, Jingkuan Song +1
Abundant real-world data can be naturally represented by large-scale networks, which demands efficient and effective learning algorithms. At the same time, labels may only be avail…
Semi-supervised Network Embedding with Differentiable Deep Quantisation
Tao He, Lianli Gao, Jingkuan Song +1
Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many downstream network analytics tasks. For large networks, the trained embeddings o…
Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation
Tao He, Lianli Gao, Jingkuan Song +2
Despite the huge progress in scene graph generation in recent years, its long-tail distribution in object relationships remains a challenging and pestering issue. Existing methods…
One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial Network
Tao He, Yuan-Fang Li, Lianli Gao +2
With the recent explosive increase of digital data, image recognition and retrieval become a critical practical application. Hashing is an effective solution to this problem, due t…
Understanding the Mechanism of Deep Learning Framework for Lesion Detection in Pathological Images with Breast Cancer
Wei-Wen Hsu, Chung-Hao Chen, Chang Hoa +7
The computer-aided detection (CADe) systems are developed to assist pathologists in slide assessment, increasing diagnosis efficiency and reducing missing inspections. Many studies…
Binary Generative Adversarial Networks for Image Retrieval
Jingkuan Song
The most striking successes in image retrieval using deep hashing have mostly involved discriminative models, which require labels. In this paper, we use binary generative adversar…
Deep Discrete Hashing with Self-supervised Pairwise Labels
Jingkuan Song, Tao He, Hangbo Fan +1
Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantl…
Deep Region Hashing for Efficient Large-scale Instance Search from Images
Jingkuan Song, Tao He, Lianli Gao +2
Instance Search (INS) is a fundamental problem for many applications, while it is more challenging comparing to traditional image search since the relevancy is defined at the insta…