Image Cropping
35 papers with code • 1 benchmarks • 4 datasets
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.
Libraries
Use these libraries to find Image Cropping models and implementationsMost implemented papers
Aesthetic-Driven Image Enhancement by Adversarial Learning
We introduce EnhanceGAN, an adversarial learning based model that performs automatic image enhancement.
Mask Editor : an Image Annotation Tool for Image Segmentation Tasks
Mask Editor allows drawing any bounding curve to mark objects and improves efficiency to mark objects with irregular shapes.
Data Augmentation using Random Image Cropping and Patching for Deep CNNs
We also confirmed that deep CNNs with RICAP achieve better results on classification tasks using CIFAR-100 and ImageNet and an image-caption retrieval task using Microsoft COCO.
Reliable and Efficient Image Cropping: A Grid Anchor based Approach
Consequently, a grid anchor based cropping benchmark is constructed, where all crops of each image are annotated and more reliable evaluation metrics are defined.
Feature Forwarding for Efficient Single Image Dehazing
Haze degrades content and obscures information of images, which can negatively impact vision-based decision-making in real-time systems.
Listwise View Ranking for Image Cropping
However, the performance of ranking-based methods is often poor and this is mainly due to two reasons: 1) image cropping is a listwise ranking task rather than pairwise comparison; 2) the rescaling caused by pooling layer and the deformation in view generation damage the performance of composition learning.
Grid Anchor based Image Cropping: A New Benchmark and An Efficient Model
The employed evaluation metrics such as intersection-over-union cannot reliably reflect the real performance of a cropping model, either.
ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection
We also compared our method with other data augmentation techniques.
Density Map Guided Object Detection in Aerial Images
Specifically, we propose a Density-Map guided object detection Network (DMNet), which is inspired from the observation that the object density map of an image presents how objects distribute in terms of the pixel intensity of the map.
Learning to Learn Cropping Models for Different Aspect Ratio Requirements
In addition, both the intermediate and final results show that the proposed model can predict different cropping windows for an image depending on different aspect ratio requirements.