mlsanityData to 7 Oct 2026

Multi-Task Learning for Visual Scene Understanding

arXiv 2203.14896 ↗First posted 28 Mar 2022cs.CV cs.AI cs.LGcited by 6

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Abstract (arXiv)

Despite the recent progress in deep learning, most approaches still go for a silo-like solution, focusing on learning each task in isolation: training a separate neural network for each individual task. Many real-world problems, however, call for a multi-modal approach and, therefore, for multi-tasking models. Multi-task learning (MTL) aims to leverage useful information across tasks to improve the generalization capability of a model. This thesis is concerned with multi-task learning in the context of computer vision. First, we review existing approaches for MTL. Next, we propose several methods that tackle important aspects of multi-task learning. The proposed methods are evaluated on various benchmarks. The results show several advances in the state-of-the-art of multi-task learning. Finally, we discuss several possibilities for future work.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 64 of its 187 references that are in the map. First posted on arXiv 28 Mar 2022.

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64 of 187 references.

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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 3.

6 papers.

  1. 2024UCIP: A Universal Framework for Compressed Image Super-Resolution using Dynamic Promptcited by 8
  2. 2025Multi-task Federated Learning with Encoder-Decoder Structure: Enabling Collaborative Learning Across Different Taskscited by 4
  3. 2024Correlation of Object Detection Performance with Visual Saliency and Depth Estimationcited by 3
  4. 2023Multi-task Highly Adaptive Lassocited by 0
  5. 2025Efficient Multi-Task Modeling through Automated Fusion of Trained Modelscited by 0
  6. 2025Semi-Supervised Multi-Task Learning for Interpretable Quality As- sessment of Fundus Imagescited by 0