Latest Research
Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation
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.
Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization
• junbaoZHUO/UODTN
• 
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.
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
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.
An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval
The application of the diffusion in many computer vision and artificial intelligence projects has been shown to give excellent improvements in performance.
Convolutional neural networks: a magic bullet for gravitational-wave detection?
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.
CornerNet-Lite: Efficient Keypoint Based Object Detection
• princeton-vl/CornerNet-Lite
•
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
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection
• wuzhe71/CPD
•
In this paper, we propose a novel Cascaded Partial Decoder (CPD) framework for fast and accurate salient object detection.
Influence Maximization via Representation Learning
• GiorgosPanagopoulos/IMINFECTOR
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.
No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement
No-reference image quality assessment (NR-IQA) aims to measure the image quality without reference image.
KPConv: Flexible and Deformable Convolution for Point Clouds
Furthermore, these locations are continuous in space and can be learned by the network.