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What Happens Without Background? Constructing Foreground-Only Data for Fine-Grained Tasks

Yuetian Wang, Wenjin Hou, Qinmu Peng, Xinge You

TL;DR

This work addresses the bias introduced by background content in fine-grained recognition by proposing an engineered pipeline that constructs foreground-only data using SAM and Detic. The approach enables controlled studies of background influence and aims to improve discriminative feature learning focused on the subject. Across datasets (CUB, Stanford Cars, Aircraft) and multiple backbones, models trained on foreground data show improvements and tighter class separations, with the Transformer-based ViT benefiting most. The method also supports expansion to additional modalities and applications, offering a practical preprocessing step for robust fine-grained analysis and future multimodal research.

Abstract

Fine-grained recognition, a pivotal task in visual signal processing, aims to distinguish between similar subclasses based on discriminative information present in samples. However, prevailing methods often erroneously focus on background areas, neglecting the capture of genuinely effective discriminative information from the subject, thus impeding practical application. To facilitate research into the impact of background noise on models and enhance their ability to concentrate on the subject's discriminative features, we propose an engineered pipeline that leverages the capabilities of SAM and Detic to create fine-grained datasets with only foreground subjects, devoid of background. Extensive cross-experiments validate this approach as a preprocessing step prior to training, enhancing algorithmic performance and holding potential for further modal expansion of the data.

What Happens Without Background? Constructing Foreground-Only Data for Fine-Grained Tasks

TL;DR

This work addresses the bias introduced by background content in fine-grained recognition by proposing an engineered pipeline that constructs foreground-only data using SAM and Detic. The approach enables controlled studies of background influence and aims to improve discriminative feature learning focused on the subject. Across datasets (CUB, Stanford Cars, Aircraft) and multiple backbones, models trained on foreground data show improvements and tighter class separations, with the Transformer-based ViT benefiting most. The method also supports expansion to additional modalities and applications, offering a practical preprocessing step for robust fine-grained analysis and future multimodal research.

Abstract

Fine-grained recognition, a pivotal task in visual signal processing, aims to distinguish between similar subclasses based on discriminative information present in samples. However, prevailing methods often erroneously focus on background areas, neglecting the capture of genuinely effective discriminative information from the subject, thus impeding practical application. To facilitate research into the impact of background noise on models and enhance their ability to concentrate on the subject's discriminative features, we propose an engineered pipeline that leverages the capabilities of SAM and Detic to create fine-grained datasets with only foreground subjects, devoid of background. Extensive cross-experiments validate this approach as a preprocessing step prior to training, enhancing algorithmic performance and holding potential for further modal expansion of the data.
Paper Structure (13 sections, 7 figures, 2 tables)

This paper contains 13 sections, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Data Construction
  3. Pipeline
  4. Error Handling
  5. Implementation Details
  6. Overview of Processed Datasets
  7. Source Datasets
  8. Data Examples
  9. Modalities Expansion Potential
  10. Experiments
  11. Feature Distribution
  12. Cross-validation Experiments
  13. Conclusion

Figures (7)

  • Figure 1: Grad-CAM Visualization of Common Backbones in Fine-Grained Classification: the first two rows for ViT and the last row for ResNet.
  • Figure 2: Proposed Pipeline for Generating Foreground Images.
  • Figure 3: Error handling.
  • Figure 4: Example of foreground data, including corresponding source images.
  • Figure 5: Extending more modalities using foreground images.
  • ...and 2 more figures