Object-Agnostic Transformers for Video Referring Segmentation
Author:
Affiliation:
1. School of Electronic Engineering, Xidian University, Xi’an, China
2. JD Explore Academy, Beijing, China
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Key Research and Development Program of Shaanxi
Fundamental Research Funds for the Central Universities
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Graphics and Computer-Aided Design,Software
Link
http://xplorestaging.ieee.org/ielx7/83/9626658/09744457.pdf?arnumber=9744457
Reference45 articles.
1. Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
2. Learning Spatiotemporal Features with 3D Convolutional Networks
3. VideoBERT: A Joint Model for Video and Language Representation Learning
4. Referring transformer: A one-step approach to multi-task visual grounding;li;arXiv 2106 03089,2021
5. TransVG: End-to-End Visual Grounding with Transformers
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3. Fully Transformer-Equipped Architecture for end-to-end Referring Video Object Segmentation;Information Processing & Management;2024-01
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