BSGAL: Gradient-Based Screening for Long-Tailed Perception Tasks

Table of Links

Abstract and 1 Introduction

1. Related work

   2.1. Generative Data Augmentation

   2.2. Active Learning and Data Analysis

2. Preliminary

3. Our method

   4.1. Estimation of Contribution in the Ideal Scenario

   4.2. Batched Streaming Generative Active Learning

4. Experiments and 5.1. Offline Setting

   5.2. Online Setting

5. Conclusion, Broader Impact, and References

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A. Implementation Details

B. More ablations

C. Discussion

D. Visualization

6. Conclusion

In this paper, we propose a new problem, how to design an effective method to realize the effective screening and utilization of generated data, to further improve the performance of downstream perception tasks. To address this problem, we propose a gradient-based generated data contribution estimation method and embed it into the actual training process. We design a complete pipeline that can automatically generate data to improve the performance of downstream perception tasks. Experiments prove that our method can achieve better performance than unfiltered or CLIP-filtered methods on long-tailed segmentation tasks.

Broader Impact

Our goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.

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:::info Authors:

(1) Muzhi Zhu, with equal contribution from Zhejiang University, China;

(2) Chengxiang Fan, with equal contribution from Zhejiang University, China;

(3) Hao Chen, Zhejiang University, China (haochen.cad@zju.edu.cn);

(4) Yang Liu, Zhejiang University, China;

(5) Weian Mao, Zhejiang University, China and The University of Adelaide, Australia;

(6) Xiaogang Xu, Zhejiang University, China;

(7) Chunhua Shen, Zhejiang University, China (chunhuashen@zju.edu.cn).

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:::info This paper is available on arxiv under CC BY-NC-ND 4.0 Deed (Attribution-Noncommercial-Noderivs 4.0 International) license.

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