Summary of Cotbal: Comprehensive Task Balancing For Multi-task Visual Instruction Tuning, by Yanqi Dai et al.
CoTBal: Comprehensive Task Balancing for Multi-Task Visual Instruction Tuning
by Yanqi Dai, Zebin You, Dong Jing, Yutian Luo, Nanyi Fei, Guoxing Yang, Zhiwu Lu
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The paper introduces a novel algorithm called Comprehensive Task Balancing (CoTBal) to mitigate the issue of suboptimal performance when learning multiple visual tasks simultaneously. The authors propose a framework that considers two critical dimensions: Inter-Task Contribution, which captures the phenomenon of knowledge overlap across tasks, and Intra-Task Difficulty, which reflects the inherent learning difficulty of each task. By quantifying these dimensions with performance-based metrics, CoTBal assigns weights to tasks based on their contributions to others and difficulty levels. The authors conduct extensive experiments on three benchmarks and demonstrate that CoTBal achieves superior and more balanced overall performance in multi-task visual instruction tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us learn better by balancing different tasks when training a model to recognize images. It’s like trying to balance multiple balls of different weights and sizes, where some balls help others roll smoothly while others need extra effort. The authors created a new way called CoTBal to make sure the model learns each task fairly and efficiently. They tested it on three sets of data and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Instruction tuning » Multi task
