인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Crowd counting estimates the number of people in an image while reliably representing individual locations. Density map-based methods are robust in dense scenes but cannot directly output individual locations, whereas the point-based P2PNet predicts head locations as point proposals and learns one-to-one assignments with ground-truth (GT) points via Hungarian matching. However, our proposal-level analysis reveals a duplicate proposal problem: multiple proposals can remain around a single GT point even when the counting error is small, so count-level metrics alone cannot evaluate structural prediction quality. The proposed ValueUp-P2PNet preserves the location and confidence prediction structure of P2PNet while predicting a uniqueness score reflecting each proposal's representativeness. Using matching results as supervision, it raises the uniqueness of representative proposals and lowers that of nearby duplicates. A Gaussian-weighted duplicate loss alleviates threshold discontinuity, and an adaptive loss-ratio curriculum adjusts the auxiliary loss contribution with training progress. On ShanghaiTech Part A, UCF-CC-50, and UCF-QNRF, ValueUp-P2PNet achieves comparable or lower MAE than the original P2PNet under the same setting while consistently increasing the matched-proposal ratio (Matched/GT) and reducing the duplicate-proposal ratio (Dup/Pred) on all three datasets. An ablation study shows that each loss term contributes differently to counting performance and to proposal-set structural quality.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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