223 citations · 354 across the 31 of their papers we have counts for
8 papers · 1 filter
Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation
Yiming Xu, Lin Chen, Zhongwei Cheng +2
We study the problem of visual question answering (VQA) in images by exploiting supervised domain adaptation, where there is a large amount of labeled data in the source domain but…
Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach
Qing Lian, Fengmao Lv, Lixin Duan +1
We propose a new approach, called self-motivated pyramid curriculum domain adaptation (PyCDA), to facilitate the adaptation of semantic segmentation neural networks from synthetic…
Adversarial Multimodal Network for Movie Question Answering
Zhaoquan Yuan, Siyuan Sun, Lixin Duan +2
Visual question answering by using information from multiple modalities has attracted more and more attention in recent years. However, it is a very challenging task, as the visual…
Deep learning enables extraction of capillary-level angiograms from single OCT volume
Jianlong Yang, Peng Liu, Lixin Duan +2
Optical coherence tomography angiography (OCTA) has drawn numerous attentions in ophthalmology. However, its data acquisition is time-consuming, because it is based on temporal-dec…
Domain Adversarial Reinforcement Learning for Partial Domain Adaptation
Jin Chen, Xinxiao Wu, Lixin Duan +1
Partial domain adaptation aims to transfer knowledge from a label-rich source domain to a label-scarce target domain which relaxes the fully shared label space assumption across di…
Known-class Aware Self-ensemble for Open Set Domain Adaptation
Qing Lian, Wen Li, Lin Chen +1
Existing domain adaptation methods generally assume different domains have the identical label space, which is quite restrict for real-world applications. In this paper, we focus o…
MiniMax Entropy Network: Learning Category-Invariant Features for Domain Adaptation
Chaofan Tao, Fengmao Lv, Lixin Duan +1
How to effectively learn from unlabeled data from the target domain is crucial for domain adaptation, as it helps reduce the large performance gap due to domain shift or distributi…
Learning Transferable Self-attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision
Xiao-Yu Zhang, Haichao Shi, Changsheng Li +3
Action recognition in videos has attracted a lot of attention in the past decade. In order to learn robust models, previous methods usually assume videos are trimmed as short seque…