STAR-GNN: Spatial-Temporal Video Representation for Content-Based Retrieval
Author:
Affiliation:
1. School of Information, Renmin University of China,Beijing,China
2. Data 61, CSIRO,Pullenvale,Australia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9859562/9858923/09859598.pdf?arnumber=9859598
Reference24 articles.
1. ViSiL: Fine-Grained Spatio-Temporal Video Similarity Learning
2. Video similarity and alignment learning on par-tial video copy detection;han;Proceedings of the 29th ACM International Conference on Multimedia,0
3. VideoWhisper: Toward Discriminative Unsupervised Video Feature Learning With Attention-Based Recurrent Neural Networks
4. Self-Supervised Spatiotemporal Learning via Video Clip Order Prediction
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1. RaSTFormer: region-aware spatiotemporal transformer for visual homogenization recognition in short videos;Neural Computing and Applications;2024-03-27
2. Performance Characterization of 2D CNN Features for Partial Video Copy Detection;Computer Analysis of Images and Patterns;2023
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