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트랜스포머 기반 판별 특징 학습 비전을 통한 얼굴 조작 감지
( Van-nhan Tran ),김민수 ( Minsu Kim ),최필주 ( Philjoo Choi ),이석환 ( Suk-hwan Lee ),( Hoanh-su Le ),권기룡 ( Ki-ryong Kwon ) 한국정보처리학회 2023 한국정보처리학회 학술대회논문집 Vol.30 No.2
Due to the serious issues posed by facial manipulation technologies, many researchers are becoming increasingly interested in the identification of face forgeries. The majority of existing face forgery detection methods leverage powerful data adaptation ability of neural network to derive distinguishing traits. These deep learning-based detection methods frequently treat the detection of fake faces as a binary classification problem and employ softmax loss to track CNN network training. However, acquired traits observed by softmax loss are insufficient for discriminating. To get over these limitations, in this study, we introduce a novel discriminative feature learning based on Vision Transformer architecture. Additionally, a separation-center loss is created to simply compress intra-class variation of original faces while enhancing inter-class differences in the embedding space.
약간 감독되는 포인트 클라우드 분석에서 일반 로컬 트랜스포머 네트워크
( Anh-thuan Tran ),이태호 ( Tae Ho Lee ),( Hoanh-su Le ),최필주 ( Philjoo Choi ),이석환 ( Suk-hwan Lee ),권기룡 ( Ki-ryong Kwon ) 한국정보처리학회 2023 한국정보처리학회 학술대회논문집 Vol.30 No.2
Due to vast points and irregular structure, labeling full points in large-scale point clouds is highly tedious and timeconsuming. To resolve this issue, we propose a novel point-based transformer network in weakly-supervised semantic segmentation, which only needs 0.1% point annotations. Our network introduces general local features, representing global factors from different neighborhoods based on their order positions. Then, we share query point weights to local features through point attention to reinforce impacts, which are essential in determining sparse point labels. Geometric encoding is introduced to balance query point impact and remind point position during training. As a result, one point in specific local areas can obtain global features from corresponding ones in other neighborhoods and reinforce from its query points. Experimental results on benchmark large-scale point clouds demonstrate our proposed network's state-of-the-art performance.