Search Results for author:
Found 8 papers, 3 papers with code
Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation
In recent works, two approaches for UVOS have been discussed that can be divided into: appearance and appearance-motion-based methods, which have limitations respectively.
Dual Prototype Attention for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (VOS) aims to detect and segment the most salient object in videos.
Ranked #2 on
Unsupervised Video Object Segmentation
on FBMS test
RT-MOT: Confidence-Aware Real-Time Scheduling Framework for Multi-Object Tracking Tasks
Focusing on multiple choices of a workload pair of detection and association, which are two main components of the tracking-by-detection approach for MOT, we tailor a measure of object confidence for RT-MOT and develop how to estimate the measure for the next frame of each MOT task.
Unsupervised Video Object Segmentation via Prototype Memory Network
The proposed model effectively extracts the RGB and motion information by extracting superpixel-based component prototypes from the input RGB images and optical flow maps.
Ranked #5 on
Unsupervised Video Object Segmentation
on FBMS test
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level.
Pixel-Level Equalized Matching for Video Object Segmentation
Feature similarity matching, which transfers the information of the reference frame to the query frame, is a key component in semi-supervised video object segmentation.
Bayesian AirComp with Sign-Alignment Precoding for Wireless Federated Learning
In this paper, we present a simple yet effective precoding method with limited channel knowledge, called sign-alignment precoding.
Bayesian Federated Learning over Wireless Networks
This paper proposes a Bayesian federated learning (BFL) algorithm to aggregate the heterogeneous quantized gradient information optimally in the sense of minimizing the mean-squared error (MSE).
