861 citations · 899 across the 7 of their papers we have counts for
7 papers
Attentional Local Contrast Networks for Infrared Small Target Detection
Yimian Dai, Yiquan Wu, Fei Zhou +1
To mitigate the issue of minimal intrinsic features for pure data-driven methods, in this paper, we propose a novel model-driven deep network for infrared small target detection, w…
Asymmetric Contextual Modulation for Infrared Small Target Detection
Yimian Dai, Yiquan Wu, Fei Zhou +1
Single-frame infrared small target detection remains a challenge not only due to the scarcity of intrinsic target characteristics but also because of lacking a public dataset. In t…
Attentional Feature Fusion
Yimian Dai, Fabian Gieseke, Stefan Oehmcke +2
Feature fusion, the combination of features from different layers or branches, is an omnipresent part of modern network architectures. It is often implemented via simple operations…
Attention as Activation
Yimian Dai, Stefan Oehmcke, Fabian Gieseke +2
Activation functions and attention mechanisms are typically treated as having different purposes and have evolved differently. However, both concepts can be formulated as a non-lin…
Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection
Yimian Dai, Yiquan Wu
Many state-of-the-art methods have been proposed for infrared small target detection. They work well on the images with homogeneous backgrounds and high-contrast targets. However,…
Laplacian regularized low rank subspace clustering
Yu Song, Yiquan Wu
The problem of fitting a union of subspaces to a collection of data points drawn from multiple subspaces is considered in this paper. In the traditional low rank representation mod…
Subspace clustering based on low rank representation and weighted nuclear norm minimization
Yu Song, Yiquan Wu
Subspace clustering refers to the problem of segmenting a set of data points approximately drawn from a union of multiple linear subspaces. Aiming at the subspace clustering proble…