Search Results for author:
Found 6 papers, 2 papers with code
Synchronizing Vision and Language: Bidirectional Token-Masking AutoEncoder for Referring Image Segmentation
While these methods match language features with image features to effectively identify likely target objects, they often struggle to correctly understand contextual information in complex and ambiguous sentences and scenes.
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
DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network
We then use a refinement strategy based on a cross-branch feed-forward network designed using a popular I3D network to refine both scores.
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection
The proposed end-to-end multi-stream architecture performs abnormal event detection with accuracy as high as 84. 48%, which is better than the performance of existing video anomaly detection methods.
Abnormal Event Detection In Video
Anomaly Detection In Surveillance Videos
+4
K-FACE: A Large-Scale KIST Face Database in Consideration with Unconstrained Environments
In this paper, we introduce a new large-scale face database from KIST, denoted as K-FACE, and describe a novel capturing device specifically designed to obtain the data.
Person Re-identification in Videos by Analyzing Spatio-Temporal Tubes
In this paper, we extract spatio-temporal sequences of frames (referred to as tubes) of moving persons and apply a multi-stage processing to match a given query tube with a gallery of stored tubes recorded through other cameras.
