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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 51–83 of 83 papers

TitleStatusHype
Automatic Image Cropping : A Computational Complexity Study—0
Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression—0
Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Network—0
Boosting Resolution Generalization of Diffusion Transformers with Randomized Positional Encodings—0
Camera View Adjustment Prediction for Improving Image Composition—0
Challenges of building medical image datasets for development of deep learning software in stroke—0
ClipCrop: Conditioned Cropping Driven by Vision-Language Model—0
Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval—0
Cropper: Vision-Language Model for Image Cropping through In-Context Learning—0
Data Augmentation using Random Image Cropping for High-resolution Virtual Try-On (VITON-CROP)—0
Deep Learning based CNN Model for Classification and Detection of Individuals Wearing Face Mask—0
Enhancing DR Classification with Swin Transformer and Shifted Window Attention—0
Find Beauty in the Rare: Contrastive Composition Feature Clustering for Nontrivial Cropping Box Regression—0
Good View Hunting: Learning Photo Composition From Dense View Pairs—0
GridNet with automatic shape prior registration for automatic MRI cardiac segmentation—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
Learning Subject-Aware Cropping by Outpainting Professional Photos—0
Learning the Change for Automatic Image Cropping—0
Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection—0
Lightweight High-Performance Blind Image Quality Assessment—0
Low Cost Machine Vision for Insect Classification—0
RWN: Robust Watermarking Network for Image Cropping Localization—0
Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation—0
Pseudo-Labeling by Multi-Policy Viewfinder Network for Image Cropping—0
Repurposing Existing Deep Networks for Caption and Aesthetic-Guided Image Cropping—0
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset—0
Rethinking Image Cropping: Exploring Diverse Compositions From Global Views—0
Image Protection for Robust Cropping Localization and Recovery—0
Semantic Image Cropping—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CACNetBDE0.03—Unverified