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Image Cropping

Image Cropping is a common photo manipulation process, which improves the overall composition by removing unwanted regions. Image Cropping is widely used in photographic, film processing, graphic design, and printing businesses.

Source: Listwise View Ranking for Image Cropping

Papers

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Showing 26–50 of 83 papers

TitleStatusHype
ACE: Anatomically Consistent Embeddings in Composition and DecompositionCode0
Aesthetic-Driven Image Enhancement by Adversarial LearningCode0
ANDA: A Novel Data Augmentation Technique Applied to Salient Object DetectionCode0
An End-to-End Neural Network for Image Cropping by Learning Composition from Aesthetic PhotosCode0
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
AugStatic - A Light-Weight Image Augmentation LibraryCode0
Construction of unbiased dental template and parametric dental model for precision digital dentistryCode0
Data Augmentation using Random Image Cropping and Patching for Deep CNNsCode0
Feature Forwarding for Efficient Single Image DehazingCode0
Learning to Learn Cropping Models for Different Aspect Ratio RequirementsCode0
Listwise View Ranking for Image CroppingCode0
Mask Editor : an Image Annotation Tool for Image Segmentation TasksCode0
Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative StudyCode0
Reliable and Efficient Image Cropping: A Grid Anchor based ApproachCode0
Understanding the Dependence of Perception Model Competency on Regions in an ImageCode0
GridNet with automatic shape prior registration for automatic MRI cardiac segmentation—0
Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval—0
ClipCrop: Conditioned Cropping Driven by Vision-Language Model—0
Challenges of building medical image datasets for development of deep learning software in stroke—0
Image Cropping under Design Constraints—0
Image Cropping with Composition and Saliency Aware Aesthetic Score Map—0
Image Cropping With Spatial-Aware Feature and Rank Consistency—0
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—0
Augmenting Model Robustness with Transformation-Invariant Attacks—0
Camera View Adjustment Prediction for Improving Image Composition—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CACNetBDE0.03—Unverified