Hui Li

TL;DR: A novel deep learning architecture for infrared and visible images fusion problems is presented, where the encoding network is combined with convolutional layers, a fusion layer, and dense block in which the output of each layer is connected to every other layer.

TL;DR: The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative; results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.

TL;DR: A novel method for infrared and visible image fusion where the nest connection-based network and spatial/channel attention models are developed that describe the importance of each spatial position and of each channel with deep features is proposed.

TL;DR: Zhang et al. as discussed by the authors proposed a multi-level image decomposition method based on latent low-rank representation (LatLRR), which is called MD LatLRR, which is used to decompose source images into detail parts and base parts.

TL;DR: A residual fusion network (RFN) which is based on a residual architecture to replace the traditional fusion approach is proposed which delivers a better performance than the state-of-the-art methods in both subjective and objective evaluation.