CTNet: Context-Based Tandem Network for Semantic Segmentation

Contextual information has been shown to be powerful for semantic segmentation. This work proposes a novel Context-based Tandem Network (CTNet) by interactively exploring the spatial contextual inf…

Fine-Grained Image Analysis With Deep Learning: A Survey

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA tar…

Learning Spatially Variant Linear Representation Models for Joint Filtering

Joint filtering mainly uses an additional guidance image as a prior and transfers its structures to the target image in the filtering process. Different from existing approaches that rely on local …

iFlowGAN : An Invertible Flow-Based Generative Adversarial Network for Unsupe…

We propose iFlowGAN that learns an invertible flow (a sequence of invertible mappings) via adversarial learning and exploit it to transform a source distribution into a target distribution for unsu…

Spatiotemporal Co-Attention Recurrent Neural Networks for Human-Skeleton Moti…

Human motion prediction aims to generate future motions based on the observed human motions. Witnessing the success of Recurrent Neural Networks (RNN) in modeling sequential data, recent works util…

Coherence Constrained Graph LSTM for Group Activity Recognition

This work aims to address the group activity recognition problem by exploring human motion characteristics. Traditional methods hold that the motions of all persons contribute equally to the group …

Physics-Based generative Adversarial Models for Image Restoration and Beyond

Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition

Video Anomaly Detection with Sparse Coding Inspired Deep Neural Networks

A Deterministic Analysis for LRR