Federated learning (FL) is a distributed learning paradigm that enables
...
We propose a novel approach for RGB-D salient instance segmentation usin...
Most previous co-salient object detection works mainly focus on extracti...
Heterogeneous graph neural networks (HGNNs) as an emerging technique hav...
Latent fingerprints are among the most important and widely used evidenc...
Recently, inspired by DETR variants, query-based end-to-end instance
seg...
Solar activity is usually caused by the evolution of solar magnetic fiel...
Graph Contrastive Learning (GCL), learning the node representations by
a...
Temporal action localization aims at localizing action instances from
un...
Most existing Graph Neural Networks (GNNs) are proposed without consider...
Graph Structure Learning (GSL) recently has attracted considerable atten...
The application of light field data in salient object de-tection is beco...
Humans perform co-saliency detection by first summarizing the consensus
...
The pursuit of power-efficiency is popularizing asymmetric multicore
pro...
Conventional salient object detection models cannot differentiate the
im...
Camouflaged object detection (COD) is a challenging task due to the low
...
Heterogeneous graph neural networks (HGNNs) as an emerging technique hav...
Recently, massive saliency detection methods have achieved promising res...
Graph convolutional networks (GCNs) have received considerable research
...
Significant performance improvement has been achieved for fully-supervis...
Albeit current salient object detection (SOD) works have achieved fantas...
How to effectively fuse cross-modal information is the key problem for R...
In this paper, we propose an online Multi-Object Tracking (MOT) approach...
In saliency detection, every pixel needs contextual information to make
...
Scheduling service order, in a very specific queueing/inventory model wi...
Traditional saliency models usually adopt hand-crafted image features an...