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Found 11 papers, 4 papers with code
Exploiting the Signal-Leak Bias in Diffusion Models
This enables us to generate images with more varied brightness, and images that better match a desired style or color.
Diffusion in Style
Not adapting this initial latent tensor to the style makes fine-tuning slow, expensive, and impractical, especially when only a few target style images are available.
What You See is What You Classify: Black Box Attributions
These attributions are provided in the form of masks that only show the classifier-relevant parts of an image, masking out the rest.
Uncertainty Surrogates for Deep Learning
We show how our approach can be used for estimating uncertainty in prediction and out-of-distribution detection.
Self-Binarizing Networks
To obtain similar binary networks, existing methods rely on the sign activation function.
Fourier-Domain Optimization for Image Processing
Image optimization problems encompass many applications such as spectral fusion, deblurring, deconvolution, dehazing, matting, reflection removal and image interpolation, among others.
Deep Feature Factorization For Concept Discovery
We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images.
Ranked #6 on
Unsupervised Human Pose Estimation
on Tai-Chi-HD
Unsupervised Facial Landmark Detection
Unsupervised Human Pose Estimation
+1
Deep Residual Network for Joint Demosaicing and Super-Resolution
By training on high-quality samples, our deep residual demosaicing and super-resolution network is able to recover high-quality super-resolved images from low-resolution Bayer mosaics in a single step without producing the artifacts common to such processing when the two operations are done separately.
Single Image Reflection Suppression
Reflections are a common artifact in images taken through glass windows.
Superpixels and Polygons Using Simple Non-Iterative Clustering
We present an improved version of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation.
Uniform Information Segmentation
Since information is a natural way of measuring image complexity, our proposed algorithm leads to image segments that are smaller and denser in areas of high complexity and larger in homogeneous regions, thus simplifying the image while preserving its details.
