The application of the diffusion in many computer vision and artificial intelligence projects has been shown to give excellent improvements in performance.
The first is based on a multi-task neural network that uses logs of diffusion cascades to embed diffusion probabilities between nodes as well as the ability of a node to create massive cascades.
Furthermore, these locations are continuous in space and can be learned by the network.
In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the gravitational-wave signature of merging black holes.
No-reference image quality assessment (NR-IQA) aims to measure the image quality without reference image.
In this paper, we propose a novel Cascaded Partial Decoder (CPD) framework for fast and accurate salient object detection.
We address the unsupervised open domain recognition (UODR) problem, where categories in labeled source domain S is only a subset of those in unlabeled target domain T. The task is to correctly classify all samples in T including known and unknown categories.
We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making.
The ability of a graph neural network (GNN) to leverage both the graph topology and graph labels is fundamental to building discriminative node and graph embeddings.
Together these two variants address the two critical use cases in efficient object detection: improving efficiency without sacrificing accuracy, and improving accuracy at real-time efficiency.
#4 best model for Real-Time Object Detection on COCO