https://github.com/eyalmichaeli/saspa-aug
공식 코드Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
The contextual bias split of the Aircraft dataset was constructed by Dunlap et al. [https://arxiv.org/abs/2305.16289]. The split uses two visually similar classes: Boeing-767 and Airbus-322. Each image in this split is categorized as “sky”, “grass”, or “road” depending on its background, with ambiguous examples filtered out. The bias in the dataset is introduced by training on 400 samples where Airbus aircraft are exclusively associated with road backgrounds and Boeing aircraft with grass backgrounds, although both types may appear against sky backgrounds.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.