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Found 27 papers, 8 papers with code
Multi-Teacher Multi-Objective Meta-Learning for Zero-Shot Hyperspectral Band Selection
However, most existing deep learning-based methods are aimed at dealing with a specific band selection dataset, and need to retrain parameters for new datasets, which significantly limits their generalizability. To address this issue, a novel multi-teacher multi-objective meta-learning network (M$^3$BS) is proposed for zero-shot hyperspectral band selection.
Back to the Color: Learning Depth to Specific Color Transformation for Unsupervised Depth Estimation
Additionally, we introduce the Syn-Real CutMix method for joint training with both real-world unsupervised and synthetic supervised depth samples, enhancing monocular depth estimation performance in real-world scenes.
Monocular Depth Estimation
Unsupervised Monocular Depth Estimation
External Knowledge Enhanced 3D Scene Generation from Sketch
Generating realistic 3D scenes is challenging due to the complexity of room layouts and object geometries. We propose a sketch based knowledge enhanced diffusion architecture (SEK) for generating customized, diverse, and plausible 3D scenes.
3D Object Detection from Point Cloud via Voting Step Diffusion
In this work, we focus on the distributional properties of point clouds and formulate the voting process as generating new points in the high-density region of the distribution of object centers.
Beyond Skeletons: Integrative Latent Mapping for Coherent 4D Sequence Generation
To address this, we propose a novel framework that generates coherent 4D sequences with animation of 3D shapes under given conditions with dynamic evolution of shape and color over time through integrative latent mapping.
SA-MixNet: Structure-aware Mixup and Invariance Learning for Scribble-supervised Road Extraction in Remote Sensing Images
To eliminate the reliance on such priors, we propose a novel Structure-aware Mixup and Invariance Learning framework (SA-MixNet) for weakly supervised road extraction that improves the model invariance in a data-driven manner.
Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement
Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs.
Vector Quantization With Self-Attention for Quality-Independent Representation Learning
During training, we enforce the quantization of features from clean and corrupted images in the same discrete embedding space so that an invariant quality-independent feature representation can be learned to improve the recognition robustness of low-quality images.
Low-Light Image Enhancement with Multi-Stage Residue Quantization and Brightness-Aware Attention
Deep learning-based LLIE methods focus on learning a mapping function between low-light images and normal-light images that outperforms conventional LLIE methods.
Self-Supervised Non-Uniform Kernel Estimation With Flow-Based Motion Prior for Blind Image Deblurring
To address these issues, we propose to represent the field of motion blur kernels in a latent space by normalizing flows, and design CNNs to predict the latent codes instead of motion kernels.
Ranked #4 on
Image Deblurring
on GoPro
Ecsnet: Spatio-temporal feature learning for event camera
To fully exploit their inherent sparsity with reconciling the spatio-temporal information, we introduce a compact event representation, namely 2D-1T event cloud sequence (2D-1T ECS).
Ranked #1 on
Gesture Generation
on DVS128 Gesture
Robust Depth Completion with Uncertainty-Driven Loss Functions
In this work, we introduce uncertainty-driven loss functions to improve the robustness of depth completion and handle the uncertainty in depth completion.
Uncertainty-Driven Loss for Single Image Super-Resolution
Specifically, we introduce variance estimation characterizing the uncertainty on a pixel-by-pixel basis into SISR solutions so the targeted pixels in a high-resolution image (mean) and their corresponding uncertainty (variance) can be learned simultaneously.
Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions
Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence.
Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution
Specifically, the cell-level search space is designed based on an information distillation mechanism, focusing on the combinations of lightweight operations and aiming to build a more lightweight and accurate SR structure.
Generalizable No-Reference Image Quality Assessment via Deep Meta-learning
Based on these two task sets, an optimization-based meta-learning is proposed to learn the generalized NR-IQA model, which can be directly used to evaluate the quality of images with unseen distortions.
Searching Efficient Model-guided Deep Network for Image Denoising
Similar to the success of NAS in high-level vision tasks, it is possible to find a memory and computationally efficient solution via NAS with highly competent denoising performance.
Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging
Different from existing GSM models using hand-crafted scale priors (e. g., the Jeffrey's prior), we propose to learn the scale prior through a deep convolutional neural network (DCNN).
Unsupervised Curriculum Domain Adaptation for No-Reference Video Quality Assessment
From this adaptation, we split the data in target domain into confident and uncertain subdomains using the proposed uncertainty-based ranking function, through measuring their prediction confidences.
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network
Deep neural networks (DNNs) based methods have achieved great success in single image super-resolution (SISR).
MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment
The underlying idea is to learn the meta-knowledge shared by human when evaluating the quality of images with various distortions, which can then be adapted to unknown distortions easily.
Learning Hybrid Sparsity Prior for Image Restoration: Where Deep Learning Meets Sparse Coding
State-of-the-art approaches toward image restoration can be classified into model-based and learning-based.
ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning
Instead of reconstructing individual blocks, the whole image is reconstructed from the linear convolutional measurements.
Denoising Prior Driven Deep Neural Network for Image Restoration
Then, the iterative process is unfolded into a deep neural network, which is composed of multiple denoisers modules interleaved with back-projection (BP) modules that ensure the observation consistencies.
Learning Parametric Sparse Models for Image Super-Resolution
More specifically, the parametric sparse prior of the desirable high-resolution (HR) image patches are learned from both the input low-resolution (LR) image and a training image dataset.
Low-Rank Tensor Approximation With Laplacian Scale Mixture Modeling for Multiframe Image Denoising
Patch-based low-rank models have shown effective in exploiting spatial redundancy of natural images especially for the application of image denoising.
Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding
Existing approaches toward Image super-resolution (SR) is often either data-driven (e. g., based on internet-scale matching and web image retrieval) or model-based (e. g., formulated as an Maximizing a Posterior estimation problem).
