What Happens Without Background? Constructing Foreground-Only Data for Fine-Grained Tasks
This addresses a specific issue in fine-grained visual recognition for researchers, but it is incremental as it builds on existing segmentation and detection methods.
The paper tackled the problem of fine-grained recognition models focusing on background noise instead of subject discriminative features by constructing foreground-only datasets using SAM and Detic, which improved algorithmic performance in cross-experiments.
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.