Search Results for author:
Found 49 papers, 18 papers with code
Cloud-Device Collaborative Adaptation to Continual Changing Environments in the Real-world
To enable the device model to deal with changing environments, we propose a new learning paradigm of Cloud-Device Collaborative Continual Adaptation, which encourages collaboration between cloud and device and improves the generalization of the device model.
BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks
In order to find them, we further propose a LiDAR-guided sampling strategy to leverage the statistical distribution of LiDAR to determine the heights of local slices.
BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 3D Object Detection
In this paper, we propose a unified framework named BEV-LGKD to transfer the knowledge in the teacher-student manner.
Multi-latent Space Alignments for Unsupervised Domain Adaptation in Multi-view 3D Object Detection
Vision-Centric Bird-Eye-View (BEV) perception has shown promising potential and attracted increasing attention in autonomous driving.
DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction
Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine.
DOLPH: Diffusion Models for Phase Retrieval
Phase retrieval refers to the problem of recovering an image from the magnitudes of its complex-valued linear measurements.
Robustness of Deep Equilibrium Architectures to Changes in the Measurement Model
Deep model-based architectures (DMBAs) are widely used in imaging inverse problems to integrate physical measurement models and learned image priors.
Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees
Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data.
SPICE: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation
However, estimation of accurate CSMs is a challenging problem when measurements are highly undersampled.
StyleSwap: Style-Based Generator Empowers Robust Face Swapping
Our core idea is to leverage a style-based generator to empower high-fidelity and robust face swapping, thus the generator's advantage can be adopted for optimizing identity similarity.
Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN
Three-dimensional fluorescence microscopy often suffers from anisotropy, where the resolution along the axial direction is lower than that within the lateral imaging plane.
Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer
Neuromorphic spike data, an upcoming modality with high temporal resolution, has shown promising potential in real-world applications due to its inherent advantage to overcome high-velocity motion blur.
Uncertainty Guided Depth Fusion for Spike Camera
In this paper, we propose a novel Uncertainty-Guided Depth Fusion (UGDF) framework to fuse the predictions of monocular and stereo depth estimation networks for spike camera.
Deep Model-Based Architectures for Inverse Problems under Mismatched Priors
While the empirical performance and theoretical properties of DMBAs have been widely investigated, the existing work in the area has primarily focused on their performance when the desired image prior is known exactly.
Efficient Meta-Tuning for Content-aware Neural Video Delivery
Our method significantly reduces the computational cost and achieves even better performance, paving the way for applying neural video delivery techniques to practical applications.
Online Deep Equilibrium Learning for Regularization by Denoising
However, the dependence of the computational/memory complexity of the measurement models in PnP/RED on the total number of measurements leaves DEQ impractical for many imaging applications.
Few-Shot Font Generation by Learning Fine-Grained Local Styles
Instead of explicitly disentangling global or component-wise modeling, the cross-attention mechanism can attend to the right local styles in the reference glyphs and aggregate the reference styles into a fine-grained style representation for the given content glyphs.
MTTrans: Cross-Domain Object Detection with Mean-Teacher Transformer
To solve this problem, we propose an end-to-end cross-domain detection Transformer based on the mean teacher framework, MTTrans, which can fully exploit unlabeled target domain data in object detection training and transfer knowledge between domains via pseudo labels.
Few-Shot Head Swapping in the Wild
Particularly, seamless blending is achieved with the help of a Semantic-Guided Color Reference Creation procedure and a Blending UNet.
Image Reconstruction for MRI using Deep CNN Priors Trained without Groundtruth
We propose a new plug-and-play priors (PnP) based MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning priors.
Adaptive Patch Exiting for Scalable Single Image Super-Resolution
Once the incremental capacity is below the threshold, the patch can exit at the specific layer.
Monotonically Convergent Regularization by Denoising
Regularization by denoising (RED) is a widely-used framework for solving inverse problems by leveraging image denoisers as image priors.
SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution
However, existing methods mostly train the DNNs on uniformly sampled LR-HR patch pairs, which makes them fail to fully exploit informative patches within the image.
Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative $R_2^\ast$ Mapping
LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of $R_2^\ast$ maps, while LEARN-BIO directly performs motion- and $B0$-inhomogeneity-corrected $R_2^\ast$ estimation.
Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation
Internet video delivery has undergone a tremendous explosion of growth over the past few years.
Deformation-Compensated Learning for Image Reconstruction without Ground Truth
Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets.
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition
The plug-and-play priors (PnP) and regularization by denoising (RED) methods have become widely used for solving inverse problems by leveraging pre-trained deep denoisers as image priors.
CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems
We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for the continuous representation of measurements.
SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees
Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems.
Contextual Graph Reasoning Networks
Graph Reasoning has shown great potential recently in modeling long-range dependencies, which are crucial for various computer vision tasks.
Joint Reconstruction and Calibration using Regularization by Denoising
Cal-RED extends the traditional RED methodology to imaging problems that require the calibration of the measurement operator.
Practical Deep Raw Image Denoising on Mobile Devices
Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets.
Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors
Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors.
Deep Image Reconstruction using Unregistered Measurements without Groundtruth
One of the key limitations in conventional deep learning based image reconstruction is the need for registered pairs of training images containing a set of high-quality groundtruth images.
Provable Convergence of Plug-and-Play Priors with MMSE denoisers
Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser.
FGSD: A Dataset for Fine-Grained Ship Detection in High Resolution Satellite Images
To further promote the research of ship detection, we introduced a new fine-grained ship detection datasets, which is named as FGSD.
Infusing Learned Priors into Model-Based Multispectral Imaging
We introduce a new algorithm for regularized reconstruction of multispectral (MS) images from noisy linear measurements.
EATEN: Entity-aware Attention for Single Shot Visual Text Extraction
Extracting entity from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts.
Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning
Most existing text reading benchmarks make it difficult to evaluate the performance of more advanced deep learning models in large vocabularies due to the limited amount of training data.
Disentangled Image Matting
Most previous image matting methods require a roughly-specificed trimap as input, and estimate fractional alpha values for all pixels that are in the unknown region of the trimap.
Online Regularization by Denoising with Applications to Phase Retrieval
Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems.
Editing Text in the Wild
Specifically, we propose an end-to-end trainable style retention network (SRNet) that consists of three modules: text conversion module, background inpainting module and fusion module.
Semi-supervised Skin Detection by Network with Mutual Guidance
In this paper we present a new data-driven method for robust skin detection from a single human portrait image.
Block Coordinate Regularization by Denoising
In this work, we develop a new block coordinate RED algorithm that decomposes a large-scale estimation problem into a sequence of updates over a small subset of the unknown variables.
Frame-Recurrent Video Inpainting by Robust Optical Flow Inference
Compared with image inpainting, performing this task on video presents new challenges such as how to preserving temporal consistency and spatial details, as well as how to handle arbitrary input video size and length fast and efficiently.
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising.
Detecting Text in the Wild with Deep Character Embedding Network
However, text in the wild is usually perspectively distorted or curved, which can not be easily tackled by existing approaches.
TextNet: Irregular Text Reading from Images with an End-to-End Trainable Network
Reading text from images remains challenging due to multi-orientation, perspective distortion and especially the curved nature of irregular text.
Image Restoration using Total Variation Regularized Deep Image Prior
In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction.
