Exploring Relational Context for Multi-Task Dense Prediction-Reference-Cited by-同舟云学术

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Exploring Relational Context for Multi-Task Dense Prediction

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Bruggemann David¹,Kanakis Menelaos¹,Obukhov Anton¹,Georgoulis Stamatios¹,Van Gool Luc¹

Affiliation:

1. ETH Zurich

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9709627/9709628/09709912.pdf?arnumber=9709912

Reference59 articles.

1. Multi-task learning as multi-objective optimization;sener;NeurIPS,2018

2. Large-scale evolution of image classifiers;real;ICML,2017

3. Attentive Single-Tasking of Multiple Tasks

4. The concrete distribution: A continuous relaxation of discrete random variables;maddison;ICLRE,2017

5. Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification

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1. A Hierarchical Multi-Task Learning Framework for Semantic Annotation in Tabular Data;Entropy;2024-08-04

2. UNITE: Multitask Learning With Sufficient Feature for Dense Prediction;IEEE Transactions on Systems, Man, and Cybernetics: Systems;2024-08

3. Improving Multiple Dense Prediction Performances by Exploiting Inter-Task Synergies for Neuromorphic Vision Sensors;IEEE Sensors Journal;2024-08-01

4. TFUT: Task fusion upward transformer model for multi-task learning on dense prediction;Computer Vision and Image Understanding;2024-07

5. When Multitask Learning Meets Partial Supervision: A Computer Vision Review;Proceedings of the IEEE;2024-06

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