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Found 9 papers, 6 papers with code
ICE-NeRF: Interactive Color Editing of NeRFs via Decomposition-Aware Weight Optimization
To perform effective color editing, we address two issues: (1) the entanglement of the implicit representation that causes unwanted color changes in undesired areas when learning weights, and (2) the loss of multi-view consistency when fine-tuning for a single or a few views.
Learning Color Representations for Low-Light Image Enhancement
To address this question, we propose a novel color representation learning method for low-light image enhancement.
Unsupervised Image Denoising with Frequency Domain Knowledge
Supervised learning-based methods yield robust denoising results, yet they are inherently limited by the need for large-scale clean/noisy paired datasets.
Variational Mutual Information Maximization Framework for VAE Latent Codes with Continuous and Discrete Priors
On top of that, the proposed framework provides a way to evaluate mutual information between latent codes and observations for a fixed VAE model.
VMI-VAE: Variational Mutual Information Maximization Framework for VAE With Discrete and Continuous Priors
On top of that, the proposed framework provides a way to evaluate mutual information between latent codes and observations for a fixed VAE model.
MHSAN: Multi-Head Self-Attention Network for Visual Semantic Embedding
Thus, in addition to the joint embedding space, we propose a novel multi-head self-attention network to capture various components of visual and textual data by attending to important parts in data.
Progressive Face Super-Resolution via Attention to Facial Landmark
Face Super-Resolution (SR) is a subfield of the SR domain that specifically targets the reconstruction of face images.
Ranked #1 on
Face Alignment
on CelebA + AFLW Unaligned
Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks
We also train the model to synthesize brain disorder MRI data to demonstrate the wide applicability of our model.
Representation of White- and Black-Box Adversarial Examples in Deep Neural Networks and Humans: A Functional Magnetic Resonance Imaging Study
However, DNNs exhibit idiosyncrasies that suggest their visual representation and processing might be substantially different from human vision.
