3 citations · 6 across the 6 of their papers we have counts for
6 papers
Graph-Boosted Attentive Network for Semantic Body Parsing
Tinghuai Wang, Huiling Wang
Human body parsing remains a challenging problem in natural scenes due to multi-instance and inter-part semantic confusions as well as occlusions. This paper proposes a novel appro…
Non-parametric Contextual Relationship Learning for Semantic Video Object Segmentation
Tinghuai Wang, Huiling Wang
We propose a novel approach for modeling semantic contextual relationships in videos. This graph-based model enables the learning and propagation of higher-level spatial-temporal c…
Spectral Graph Reasoning Network for Hyperspectral Image Classification
Huiling Wang
Convolutional neural networks (CNNs) have achieved remarkable performance in hyperspectral image (HSI) classification over the last few years. Despite the progress that has been ma…
Simultaneously Learning Architectures and Features of Deep Neural Networks
Tinghuai Wang, Lixin Fan, Huiling Wang
This paper presents a novel method which simultaneously learns the number of filters and network features repeatedly over multiple epochs. We propose a novel pruning loss to explic…
Translate-to-Recognize Networks for RGB-D Scene Recognition
Dapeng Du, Limin Wang, Huiling Wang +2
Cross-modal transfer is helpful to enhance modality-specific discriminative power for scene recognition. To this end, this paper presents a unified framework to integrate the tasks…
Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation
Huiling Wang, Tapani Raiko, Lasse Lensu +2
Deep convolutional neural networks (CNNs) have been immensely successful in many high-level computer vision tasks given large labeled datasets. However, for video semantic object s…