339 citations · 1.4k across the 32 of their papers we have counts for
17 papers · 1 filter
Enhanced Modality Transition for Image Captioning
Ziwei Wang, Yadan Luo, Zi Huang
Image captioning model is a cross-modality knowledge discovery task, which targets at automatically describing an image with an informative and coherent sentence. To generate the c…
Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches
Zhi Chen, Sen Wang, Jingjing Li +1
Zero-shot learning (ZSL) is commonly used to address the very pervasive problem of predicting unseen classes in fine-grained image classification and other tasks. One family of sol…
Adversarial Bipartite Graph Learning for Video Domain Adaptation
Yadan Luo, Zi Huang, Zijian Wang +2
Domain adaptation techniques, which focus on adapting models between distributionally different domains, are rarely explored in the video recognition area due to the significant sp…
Progressive Graph Learning for Open-Set Domain Adaptation
Yadan Luo, Zijian Wang, Zi Huang +1
Domain shift is a fundamental problem in visual recognition which typically arises when the source and target data follow different distributions. The existing domain adaptation ap…
ORD: Object Relationship Discovery for Visual Dialogue Generation
Ziwei Wang, Zi Huang, Yadan Luo +1
With the rapid advancement of image captioning and visual question answering at single-round level, the question of how to generate multi-round dialogue about visual content has no…
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…
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…
Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning
Yadan Luo, Ziwei Wang, Zi Huang +2
Rich high-quality annotated data is critical for semantic segmentation learning, yet acquiring dense and pixel-wise ground-truth is both labor- and time-consuming. Coarse annotatio…
Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search
Lei Zhu, Zi Huang, Xiaobai Liu +3
Mobile landmark search (MLS) recently receives increasing attention for its great practical values. However, it still remains unsolved due to two important challenges. One is high…
Where to Focus: Query Adaptive Matching for Instance Retrieval Using Convolutional Feature Maps
Jiewei Cao, Lingqiao Liu, Peng Wang +3
Instance retrieval requires one to search for images that contain a particular object within a large corpus. Recent studies show that using image features generated by pooling conv…