Summary of Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition, by Zhiyong Yang et al.
Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition
by Zhiyong Yang, Qianqian Xu, Zitai Wang, Sicong Li, Boyu Han, Shilong Bao, Xiaochun Cao, Qingming Huang
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 investigates test-agnostic long-tail recognition, a challenging task where the test label distributions are unknown and arbitrarily imbalanced. The authors argue that these distributions can be broken down hierarchically into global and local levels, with global variations reflecting broader diversity and local variations arising from milder changes focused on specific neighbors. Traditional methods primarily use Mixture-of-Expert (MoE) approaches targeting a few fixed test label distributions with substantial global variations, neglecting local variations. To address this issue, the authors propose a new MoE strategy called DirMixE, which assigns experts to different Dirichlet meta-distributions of the label distribution, each targeting specific aspects of local variations. The diversity among these Dirichlet meta-distributions inherently captures global variations, leading to a more stable objective function and better sampling of test distributions to quantify mean and variance performance outcomes. Theoretically, the authors show that their proposed objective benefits from enhanced generalization through variance-based regularization. Comprehensive experiments across multiple benchmarks confirm the effectiveness of DirMixE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about recognizing things in pictures even if they’re not common or familiar. It’s hard because we don’t know what to expect and some things might be similar but a little different. The researchers looked at how this problem can be broken down into smaller parts, like global changes that make lots of differences and local changes that make small differences. They came up with a new way to do this called DirMixE, which is like a team of experts who work together to recognize things in pictures. This approach helps the computer learn better by considering both the big changes and the small changes. The results show that this method works well across many different datasets. |
Keywords
» Artificial intelligence » Generalization » Objective function » Regularization
