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Found 19 papers, 7 papers with code
Colorizing Monochromatic Radiance Fields
Though Neural Radiance Fields (NeRF) can produce colorful 3D representations of the world by using a set of 2D images, such ability becomes non-existent when only monochromatic images are provided.
Spy-Watermark: Robust Invisible Watermarking for Backdoor Attack
Backdoor attack aims to deceive a victim model when facing backdoor instances while maintaining its performance on benign data.
Gene-induced Multimodal Pre-training for Image-omic Classification
Histology analysis of the tumor micro-environment integrated with genomic assays is the gold standard for most cancers in modern medicine.
SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting
Given the coordinates of a pixel we want to reconstruct, we first collect its neighboring pixels in the input image and extract their detail-enhanced semantic embeddings, unmask-attentional semantic embeddings, importance values, and spatial distances to the desired pixel.
CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields
Neural Radiance Fields (NeRF) have the potential to be a major representation of media.
Enhancing Low-Light Images Using Infrared-Encoded Images
In this work, we propose a novel approach to increase the visibility of images captured under low-light environments by removing the in-camera infrared (IR) cut-off filter, which allows for the capture of more photons and results in improved signal-to-noise ratio due to the inclusion of information from the IR spectrum.
The Age of Synthetic Realities: Challenges and Opportunities
Synthetic realities are digital creations or augmentations that are contextually generated through the use of Artificial Intelligence (AI) methods, leveraging extensive amounts of data to construct new narratives or realities, regardless of the intent to deceive.
Robust Cross-domain CT Image Reconstruction via Bayesian Noise Uncertainty Alignment
Under this probabilistic framework, we propose to alleviate the noise distribution shifts between source and target domains via implicit noise modeling schemes in the latent space and image space, respectively.
Removing Image Artifacts From Scratched Lens Protectors
Removing image artifacts from the scratched lens protector is inherently challenging due to the occasional flare artifacts and the co-occurring interference within mixed artifacts.
Occlusion-Free Scene Recovery via Neural Radiance Fields
Our everyday lives are filled with occlusions that we strive to see through.
Enhancing Low-Light Images in Real World via Cross-Image Disentanglement
Images captured in the low-light condition suffer from low visibility and various imaging artifacts, e. g., real noise.
Learning Meta Pattern for Face Anti-Spoofing
To improve the generalization ability, recent hybrid methods have been explored to extract task-aware handcrafted features (e. g., Local Binary Pattern) as discriminative information for the input of DNNs.
Low-Light Image Enhancement with Normalizing Flow
To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them is one-to-many.
Ranked #3 on
Low-Light Image Enhancement
on Sony-Total-Dark
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization
Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks.
Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems
In this paper, we propose a novel black-box backdoor attack technique on face recognition systems, which can be conducted without the knowledge of the targeted DNN model.
Reflection Scene Separation From a Single Image
We first propose a strategy to obtain such ground truth and its corresponding input images.
Face Image Reflection Removal
Face images captured through the glass are usually contaminated by reflections.
CRRN: Multi-Scale Guided Concurrent Reflection Removal Network
Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks.
Benchmarking Single-Image Reflection Removal Algorithms
Removing undesired reflections from a photo taken in front of a glass is of great importance for enhancing the efficiency of visual computing systems.
