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Found 6 papers, 3 papers with code
Occluded Person Re-Identification via Relational Adaptive Feature Correction Learning
Occluded person re-identification (Re-ID) in images captured by multiple cameras is challenging because the target person is occluded by pedestrians or objects, especially in crowded scenes.
Tackling Background Distraction in Video Object Segmentation
Semi-supervised video object segmentation (VOS) aims to densely track certain designated objects in videos.
Pixel-Level Bijective Matching for Video Object Segmentation
Before finding the best matches for the query frame pixels, the optimal matches for the reference frame pixels are first considered to prevent each reference frame pixel from being overly referenced.
Multi-object tracking with self-supervised associating network
So in this paper, we propose a novel self-supervised learning method using a lot of short videos which has no human labeling, and improve the tracking performance through the re-identification network trained in the self-supervised manner to solve the lack of training data problem.
PMVOS: Pixel-Level Matching-Based Video Object Segmentation
Semi-supervised video object segmentation (VOS) aims to segment arbitrary target objects in video when the ground truth segmentation mask of the initial frame is provided.
CRVOS: Clue Refining Network for Video Object Segmentation
The encoder-decoder based methods for semi-supervised video object segmentation (Semi-VOS) have received extensive attention due to their superior performances.
Ranked #60 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
