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Image Retargeting
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Prune and Repaint: Content-Aware Image Retargeting for any Ratio
Retargeting video with an end-to-end framework
Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions
OAIR: Object-Aware Image Retargeting Using PSO and Aesthetic Quality Assessment
Fast Hybrid Image Retargeting
SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery
Instance-Level Relative Saliency Ranking with Graph Reasoning
Self-Play Reinforcement Learning for Fast Image Retargeting
Deep Convolutional Neural Network for Identifying Seam-Carving Forgery
Image Seam-Carving by Controlling Positional Distribution of Seams
DCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting
Context-Aware Saliency Detection for Image Retargeting Using Convolutional Neural Networks
Cycle-IR: Deep Cyclic Image Retargeting
DeepIR: A Deep Semantics Driven Framework for Image Retargeting
Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos
Image Retargetability
Image retargeting via Beltrami representation
Novel Evaluation Metrics for Seam Carving based Image Retargeting
Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting
Pixel Objectness
Engineering Deep Representations for Modeling Aesthetic Perception
Subjects and Their Objects: Localizing Interactees for a Person-Centric View of Importance
Image Retargeting by Content-Aware Synthesis
Efficient Image Retargeting for High Dynamic Range Scenes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.