핵심 개념
Proposing a one-stage weakly supervised grounded captioner with a relation module for accurate captioning and grounding.
초록
The article introduces a one-stage weakly supervised grounded image captioning method that directly processes RGB images for captioning and grounding. It incorporates a relation module to enhance the understanding of relations between objects, leading to improved captioning and grounding performance. The proposed method achieves state-of-the-art grounding performance on challenging datasets.
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Introduction to Weakly Supervised Grounded Image Captioning
- Aim: Generate captions and localize objects without bounding box supervision.
- Challenges with existing two-stage pipelines.
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Methodology
- Proposal of a one-stage weakly supervised grounded captioner.
- Utilization of a relation module for multi-label classification.
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Experimental Results
- Validation on Flick30k Entities and MSCOCO captioning datasets.
- Achieving state-of-the-art grounding and competitive captioning performance.
통계
최근 두 단계 솔루션은 주로 오프더셀프 객체 탐지기를 적용하여 입력 이미지를 여러 영역 특성으로 인코딩합니다.
제안된 방법은 두 가지 도전적인 데이터 세트에서 최첨단의 미세 조정 성능을 달성합니다.
인용구
"We propose a one-stage weakly supervised grounded captioner that directly takes the RGB image as input."
"The relation semantics aid the prediction of relation words in the caption."