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Found 9 papers, 5 papers with code
Trainable Self-Guided Filter for Multi-Focus Image Fusion
The proposed model comprises an encoder-decoder network, and a trainable self-guided filtering (TSGF) module that is specifically designed to enhance spatial consistency in the predicted focus map and to eliminate the requirements of post-processing in existing GAN-based methods.
Multi-image Tranformer for Multi-focus Image Fusion
We develop a Multi-image Transformer (MiT) for MFIF by being inspired by a Spatial-Temporal Transformer Network (STTN) so that global connection can be modeled along multiple input images.
Region contrastive camera localization
The proposed approach maps image features from different camera views of the same 3D region to nearby points in the learned feature space.
VidStyleODE: Disentangled Video Editing via StyleGAN and NeuralODEs
However, the applicability of such advancements to the video domain has been hindered by the difficulty of representing and controlling videos in the latent space of GANs.
Disentangling Content and Motion for Text-Based Neural Video Manipulation
Giving machines the ability to imagine possible new objects or scenes from linguistic descriptions and produce their realistic renderings is arguably one of the most challenging problems in computer vision.
Manipulating Attributes of Natural Scenes via Hallucination
In this study, we explore building a two-stage framework for enabling users to directly manipulate high-level attributes of a natural scene.
Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks
The proposed approach preserves high-frequency details via an adversarial loss; and it offers enhanced synthesis performance via a pixel-wise loss for registered multi-contrast images and a cycle-consistency loss for unregistered images.
Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts
Automatic image synthesis research has been rapidly growing with deep networks getting more and more expressive.
Image Matting With KL-Divergence Based Sparse Sampling
Previous sampling-based image matting methods typically rely on certain heuristics in collecting representative samples from known regions, and thus their performance deteriorates if the underlying assumptions are not satisfied.
