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Image Cropping
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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.
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Boosting Resolution Generalization of Diffusion Transformers with Randomized Positional Encodings
ACE: Anatomically Consistent Embeddings in Composition and Decomposition
FiTv2: Scalable and Improved Flexible Vision Transformer for Diffusion Model
Cropper: Vision-Language Model for Image Cropping through In-Context Learning
Hallu-PI: Evaluating Hallucination in Multi-modal Large Language Models within Perturbed Inputs
Understanding the Dependence of Perception Model Competency on Regions in an Image
Pseudo-Labeling by Multi-Policy Viewfinder Network for Image Cropping
Low Cost Machine Vision for Insect Classification
FiT: Flexible Vision Transformer for Diffusion Model
Spatial-Semantic Collaborative Cropping for User Generated Content
Learning Subject-Aware Cropping by Outpainting Professional Photos
Deep Learning based CNN Model for Classification and Detection of Individuals Wearing Face Mask
Image Cropping under Design Constraints
Challenges of building medical image datasets for development of deep learning software in stroke
Beyond Image Borders: Learning Feature Extrapolation for Unbounded Image Composition
Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection
Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer
Construction of unbiased dental template and parametric dental model for precision digital dentistry
Lightweight High-Performance Blind Image Quality Assessment
Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy
Find Beauty in the Rare: Contrastive Composition Feature Clustering for Nontrivial Cropping Box Regression
An Experience-based Direct Generation approach to Automatic Image Cropping
ClipCrop: Conditioned Cropping Driven by Vision-Language Model
Human-centric Image Cropping with Partition-aware and Content-preserving Features
Image Protection for Robust Cropping Localization and Recovery
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset
Repurposing Existing Deep Networks for Caption and Aesthetic-Guided Image Cropping
Data Augmentation using Random Image Cropping for High-resolution Virtual Try-On (VITON-CROP)
From Image to Imuge: Immunized Image Generation
RWN: Robust Watermarking Network for Image Cropping Localization
An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences
Semantic Image Cropping
Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images
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