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Found 7 papers, 3 papers with code
Lightweight network towards real-time image denoising on mobile devices
In this paper, we identify the real bottlenecks that affect the CNN-based models' run-time performance on mobile devices: memory access cost and NPU-incompatible operations, and build the model based on these.
SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement
Image-adaptive lookup tables (LUTs) have achieved great success in real-time image enhancement tasks due to their high efficiency for modeling color transforms.
Ranked #5 on
Image Enhancement
on MIT-Adobe 5k
(PSNR on proRGB metric)
AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement
They adopt a sub-optimal uniform sampling point allocation, limiting the expressiveness of the learned LUTs since the (tri-)linear interpolation between uniform sampling points in the LUT transform might fail to model local non-linearities of the color transform.
Ranked #2 on
Photo Retouching
on MIT-Adobe 5k
Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction
When applying multi-instance learning (MIL) to make predictions for bags of instances, the prediction accuracy of an instance often depends on not only the instance itself but also its context in the corresponding bag.
Self Supervised Lesion Recognition For Breast Ultrasound Diagnosis
Since an ultrasound image only describes a partial 2D projection of a 3D lesion, such paradigm ignores the semantic relationship between different views of a lesion, which is inconsistent with the traditional diagnosis where sonographers analyze a lesion from at least two views.
Decoupled Gradient Harmonized Detector for Partial Annotation: Application to Signet Ring Cell Detection
Early diagnosis of signet ring cell carcinoma dramatically improves the survival rate of patients.
Reinventing 2D Convolutions for 3D Images
Theoretically, ANY 2D CNN (ResNet, DenseNet, or DeepLab) is able to be converted into a 3D ACS CNN, with pretrained weight of a same parameter size.
