Summary of Cross-task Affinity Learning For Multitask Dense Scene Predictions, by Dimitrios Sinodinos et al.
Cross-Task Affinity Learning for Multitask Dense Scene Predictions
by Dimitrios Sinodinos, Narges Armanfard
First submitted to arxiv on: 20 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a new framework for multitask learning called Cross-Task Affinity Learning (CTAL), which enhances task refinement in multitask networks by capturing local and long-range cross-task interactions. The CTAL module uses parameter-efficient grouped convolutions to optimize task affinity matrices, achieving state-of-the-art performance for both CNN and transformer backbones while using significantly fewer parameters than single-task learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for computers to learn multiple tasks at the same time, which can help them get better at each individual task. The new framework called CTAL helps computers understand how different tasks are related to each other, which makes them even better at all of those tasks. This is a big deal because it could be used in lots of areas where we want machines to learn and improve quickly. |
Keywords
* Artificial intelligence * Cnn * Parameter efficient * Transformer
