PAC-FNO: Parallel-Structured Fourier Neural Operators for Low-Quality Image Recognition at ICLR 2024

Основные понятия

Proposing PAC-FNO for handling low-quality images with varying resolutions and natural variations in a single model.

Аннотация

Abstract:

Traditional image recognition models struggle with low-quality inputs due to resolution differences and natural variations.
PAC-FNO operates in the frequency domain, improving performance on images with various resolutions and variations.
Two-stage training algorithm fine-tunes PAC-FNO with existing models for enhanced performance.
Introduction:

Deep neural networks have revolutionized visual recognition but face challenges with low-quality inputs.
PAC-FNO addresses resolution and input variation challenges in image recognition models.
PAC-FNO Design:

AC-FNO block without band pass filters for image semantics.
Parallel configuration of AC-FNO blocks for increased capacity in learning input variations.
Evaluation:

PAC-FNO outperforms baselines in handling low-resolution images and input variations.
Superior performance in fine-grained datasets and resilience to input variations demonstrated.
Further Work:

Future applications of PAC-FNO in complex real-world scenarios with multiple input degradations.

Статистика

"Extensively evaluating methods with seven image recognition benchmarks, we show that the proposed PAC-FNO improves the performance of existing baseline models on images with various resolutions by up to 77.1% and various types of natural variations in the images at inference."
"The parameters of PAC-FNO for various backbone models are reported in the Appendix §F.2."

Цитаты

"PAC-FNO provides two advantages over existing methods: (i) It can handle both low-resolution and input variations typically observed in low-quality images with a single model; (ii) One can attach PAC-FNO to any visual recognition model and fine-tune it."
"Our proposed parallel configuration of AC-FNO blocks is effective in low-resolution image recognition."

Ключевые выводы из

PAC-FNO

by Jinsung Jeon... в arxiv.org 03-05-2024

https://arxiv.org/pdf/2402.12721.pdf

Дополнительные вопросы

질문 1

PAC-FNO을 동적 입력 변화에 대응할 수 있는 실시간 응용 프로그램에 어떻게 적응시킬 수 있을까요?
Answer 1 here

질문 2

PAC-FNO을 민감한 이미지 인식 작업에 배포할 때 고려해야 할 윤리적 고려 사항은 무엇인가요?
Answer 2 here

질문 3

PAC-FNO의 개념을 이미지 인식 이외의 다른 영역으로 확장하여 성능을 향상시키는 방법은 무엇일까요?
Answer 3 here