339 citations · 1.4k across the 32 of their papers we have counts for
14 papers · 1 filter
Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling
Yadan Luo, Zi Huang, Zheng Zhang +3
Meta-learning for few-shot learning allows a machine to leverage previously acquired knowledge as a prior, thus improving the performance on novel tasks with only small amounts of…
Deep Collaborative Discrete Hashing with Semantic-Invariant Structure
Zijian Wang, Zheng Zhang, Yadan Luo +1
Existing deep hashing approaches fail to fully explore semantic correlations and neglect the effect of linguistic context on visual attention learning, leading to inferior performa…
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
Ruihong Qiu, Jingjing Li, Zi Huang +1
Predicting a user's preference in a short anonymous interaction session instead of long-term history is a challenging problem in the real-life session-based recommendation, e.g., e…
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
Shaoxiong Ji, Shirui Pan, Xue Li +3
Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people's life. Current suicidal ideation detection met…
CANZSL: Cycle-Consistent Adversarial Networks for Zero-Shot Learning from Natural Language
Zhi Chen, Jingjing Li, Yadan Luo +2
Existing methods using generative adversarial approaches for Zero-Shot Learning (ZSL) aim to generate realistic visual features from class semantics by a single generative network,…
Cycle-consistent Conditional Adversarial Transfer Networks
Jingjing Li, Erpeng Chen, Zhengming Ding +3
Domain adaptation investigates the problem of cross-domain knowledge transfer where the labeled source domain and unlabeled target domain have distinctive data distributions. Recen…
Alleviating Feature Confusion for Generative Zero-shot Learning
Jingjing Li, Mengmeng Jing, Ke Lu +3
Lately, generative adversarial networks (GANs) have been successfully applied to zero-shot learning (ZSL) and achieved state-of-the-art performance. By synthesizing virtual unseen…
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation
Yadan Luo, Zi Huang, Zheng Zhang +3
Visual paragraph generation aims to automatically describe a given image from different perspectives and organize sentences in a coherent way. In this paper, we address three criti…
Agile Domain Adaptation
Jingjing Li, Mengmeng Jing, Yue Xie +2
Domain adaptation investigates the problem of leveraging knowledge from a well-labeled source domain to an unlabeled target domain, where the two domains are drawn from different d…
From Zero-Shot Learning to Cold-Start Recommendation
Jingjing Li, Mengmeng Jing, Ke Lu +3
Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are independentl…
Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval
Lei Zhu, Zi Huang, Zhihui Li +2
Unsupervised hashing can desirably support scalable content-based image retrieval (SCBIR) for its appealing advantages of semantic label independence, memory and search efficiency.…
SADIH: Semantic-Aware DIscrete Hashing
Zheng Zhang, Guo-sen Xie, Yang Li +2
Due to its low storage cost and fast query speed, hashing has been recognized to accomplish similarity search in large-scale multimedia retrieval applications. Particularly supervi…
Leveraging the Invariant Side of Generative Zero-Shot Learning
Jingjing Li, Mengmeng Jin, Ke Lu +3
Conventional zero-shot learning (ZSL) methods generally learn an embedding, e.g., visual-semantic mapping, to handle the unseen visual samples via an indirect manner. In this paper…
Snap and Find: Deep Discrete Cross-domain Garment Image Retrieval
Yadan Luo, Ziwei Wang, Zi Huang +2
With the increasing number of online stores, there is a pressing need for intelligent search systems to understand the item photos snapped by customers and search against large-sca…