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Found 12 papers, 4 papers with code
DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup Tables
While deep neural networks have revolutionized image denoising capabilities, their deployment on edge devices remains challenging due to substantial computational and memory requirements.
OccluGaussian: Occlusion-Aware Gaussian Splatting for Large Scene Reconstruction and Rendering
Cameras in such regions exhibit stronger correlations and a higher average contribution, facilitating high-quality scene reconstruction.
Decoupling Appearance Variations with 3D Consistent Features in Gaussian Splatting
We validate our method on several appearance-variant scenes, and demonstrate that it achieves state-of-the-art rendering quality with minimal training time and memory usage, without compromising rendering speeds.
Poisoning-based Backdoor Attacks for Arbitrary Target Label with Positive Triggers
To exploit the abundant information contained in the input data to output label mapping, our scheme utilizes the network trained from the clean dataset as a trigger generator to produce poisons that significantly raise the success rate of backdoor attacks versus conventional approaches.
Taming Lookup Tables for Efficient Image Retouching
Existing enhancement models often optimize for high performance while falling short of reducing hardware inference time and power consumption, especially on edge devices with constrained computing and storage resources.
Learning Spatially Collaged Fourier Bases for Implicit Neural Representation
Existing approaches to Implicit Neural Representation (INR) can be interpreted as a global scene representation via a linear combination of Fourier bases of different frequencies.
Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution
Conventional super-resolution (SR) schemes make heavy use of convolutional neural networks (CNNs), which involve intensive multiply-accumulate (MAC) operations, and require specialized hardware such as graphics processing units.
Lite it fly: An All-Deformable-Butterfly Network
Most deep neural networks (DNNs) consist fundamentally of convolutional and/or fully connected layers, wherein the linear transform can be cast as the product between a filter matrix and a data matrix obtained by arranging feature tensors into columns.
A Spectral Perspective towards Understanding and Improving Adversarial Robustness
Deep neural networks (DNNs) are incredibly vulnerable to crafted, imperceptible adversarial perturbations.
Frequency Regularization for Improving Adversarial Robustness
Deep neural networks are incredibly vulnerable to crafted, human-imperceptible adversarial perturbations.
Multimodal Transformer for Automatic 3D Annotation and Object Detection
By enriching the sparse point clouds, our method achieves 4. 48\% and 4. 03\% better 3D AP on KITTI moderate and hard samples, respectively, versus the state-of-the-art autolabeler.
What Do Adversarially trained Neural Networks Focus: A Fourier Domain-based Study
We are hopeful this work can shed light on the design of more robust neural networks.
