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Found 62 papers, 34 papers with code
SGCNeRF: Few-Shot Neural Rendering via Sparse Geometric Consistency Guidance
Neural Radiance Field (NeRF) technology has made significant strides in creating novel viewpoints.
Exploiting Self-Supervised Constraints in Image Super-Resolution
Recent advances in self-supervised learning, predominantly studied in high-level visual tasks, have been explored in low-level image processing.
REPS: Reconstruction-based Point Cloud Sampling
Our method outperforms previous approaches in preserving the structural features of the sampled point clouds.
Stealing Stable Diffusion Prior for Robust Monocular Depth Estimation
This paper introduces a novel approach named Stealing Stable Diffusion (SSD) prior for robust monocular depth estimation.
OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition
Depression Recognition (DR) poses a considerable challenge, especially in the context of the growing concerns surrounding privacy.
OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction
This technical report presents our solution, "occTransformer" for the 3D occupancy prediction track in the autonomous driving challenge at CVPR 2023.
SDGE: Stereo Guided Depth Estimation for 360$^\circ$ Camera Sets
We suggest building virtual pinhole cameras to resolve the distortion problem of fisheye cameras and unify the processing for the two types of 360$^\circ$ cameras.
Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarios
Monocular depth estimation from RGB images plays a pivotal role in 3D vision.
A Comprehensive Survey on 3D Content Generation
Recent years have witnessed remarkable advances in artificial intelligence generated content(AIGC), with diverse input modalities, e. g., text, image, video, audio and 3D.
Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural Image Compression
Recent advancements in neural compression have surpassed traditional codecs in PSNR and MS-SSIM measurements.
Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning
Using the measurement, we analyze existing techniques for inverting samples and get some insightful information that inspires a novel loss function to reduce the inconsistency.
Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach
Recent progress in single-image super-resolution (SISR) has achieved remarkable performance, yet the computational costs of these methods remain a challenge for deployment on resource-constrained devices.
Ranked #29 on
Image Super-Resolution
on Manga109 - 4x upscaling
HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models
In the specific, our model is constructed based on Latent Diffusion Model (LDM) and elaborately designed to absorb content and style instance as conditions of LDM.
On the Dynamics Under the Unhinged Loss and Beyond
In this paper, we introduce the unhinged loss, a concise loss function, that offers more mathematical opportunities to analyze the closed-form dynamics while requiring as few simplifications or assumptions as possible.
SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data
This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023).
Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration
Our approach, named Model Contrastive Learning for Image Restoration (MCLIR), rejuvenates latency models as negative models, making it compatible with diverse image restoration tasks.
Ranked #10 on
Image Super-Resolution
on Manga109 - 4x upscaling
Fully $1\times1$ Convolutional Network for Lightweight Image Super-Resolution
By incorporating a parameter-free spatial-shift operation, it equips the fully $1\times1$ convolutional network with powerful representation capability while impressive computational efficiency.
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
Retinex-based Image Denoising / Contrast Enhancement using Gradient Graph Laplacian Regularizer
Images captured in poorly lit conditions are often corrupted by acquisition noise.
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study
Large amounts of incremental learning algorithms have been proposed to alleviate the catastrophic forgetting issue arises while dealing with sequential data on a time series.
Super-Resolving Face Image by Facial Parsing Information
In this paper, we build a novel parsing map guided face super-resolution network which extracts the face prior (i. e., parsing map) directly from low-resolution face image for the following utilization.
Incorporating Transformer Designs into Convolutions for Lightweight Image Super-Resolution
Building upon the NA module, we propose a lightweight single image super-resolution (SISR) network named TCSR.
Image Deblurring by Exploring In-depth Properties of Transformer
We demonstrate the effectiveness of transformer properties in improving the perceptual quality while not sacrificing the quantitative scores (PSNR) over the most competitive models, such as Uformer, Restormer, and NAFNet, on defocus deblurring and motion deblurring tasks.
Augment and Criticize: Exploring Informative Samples for Semi-Supervised Monocular 3D Object Detection
In this paper, we improve the challenging monocular 3D object detection problem with a general semi-supervised framework.
Guided Depth Map Super-resolution: A Survey
Guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image, is a longstanding and fundamental problem, it has attracted considerable attention from computer vision and image processing communities.
Spatial-Frequency Mutual Learning for Face Super-Resolution
To circumvent this problem, Fourier transform is introduced, which can capture global facial structure information and achieve image-size receptive field.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation
During the iterative update, the results of depth estimation are compared across cameras and the information of overlapping areas is propagated to the whole depth maps with the help of basis formulation.
Deep Lossy Plus Residual Coding for Lossless and Near-lossless Image Compression
In the lossless mode, the DLPR coding system first performs lossy compression and then lossless coding of residuals.
LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devices
Notably, our solution named LiteDepth ranks 2nd in the MAI&AIM2022 Monocular Depth Estimation Challenge}, with a si-RMSE of 0. 311, an RMSE of 3. 79, and the inference time is 37$ms$ tested on the Raspberry Pi 4.
Learning Towards the Largest Margins
One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power.
Prototype-Anchored Learning for Learning with Imperfect Annotations
We verify the effectiveness of PAL on class-imbalanced learning and noise-tolerant learning by extensive experiments on synthetic and real-world datasets.
ReSmooth: Detecting and Utilizing OOD Samples when Training with Data Augmentation
Further, we incorporate our ReSmooth framework with negative data augmentation strategies.
Towards Model Generalization for Monocular 3D Object Detection
However, caused by severe domain gaps (e. g., the field of view (FOV), pixel size, and object size among datasets), Mono3D detectors have difficulty in generalization, leading to drastic performance degradation on unseen domains.
GLaMa: Joint Spatial and Frequency Loss for General Image Inpainting
Our proposed GLaMa can better capture different types of missing information by using more types of masks.
Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training
Based on this, we develop a teacher-student paradigm to generate adaptive pseudo labels on the target domain.
Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation
Most existing methods either take the end-to-end supervised learning based manner, where large amounts of pairs of sparse input and dense ground-truth are exploited as supervision information; or treat up-scaling of different scale factors as independent tasks, and have to build multiple networks to handle upsampling with varying factors.
BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation
Recently, some methods reformulate it as a classification-regression task to boost the model performance, where continuous depth is estimated via a linear combination of predicted probability distributions and discrete bins.
Ranked #20 on
Monocular Depth Estimation
on KITTI Eigen split
(using extra training data)
DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation
This paper aims to address the problem of supervised monocular depth estimation.
Ranked #20 on
Monocular Depth Estimation
on KITTI Eigen split
(using extra training data)
Exploiting the Potential of Datasets: A Data-Centric Approach for Model Robustness
Robustness of deep neural networks (DNNs) to malicious perturbations is a hot topic in trustworthy AI.
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon
A new type of non-invasive attacks emerged recently, which attempt to cast perturbation onto the target by optics based tools, such as laser beam and projector.
Towards End-to-End Image Compression and Analysis with Transformers
Meanwhile, we propose a feature aggregation module to fuse the compressed features with the selected intermediate features of the Transformer, and feed the aggregated features to a deconvolutional neural network for image reconstruction.
Deep Attentional Guided Image Filtering
Specifically, we propose an attentional kernel learning module to generate dual sets of filter kernels from the guidance and the target, respectively, and then adaptively combine them by modeling the pixel-wise dependency between the two images.
SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations
To bridge this gap, we aim to learn a spatial-aware visual representation that can describe the three-dimensional space and is more suitable and effective for these tasks.
Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution
Learning-based methods usually use a convolutional neural network (CNN) to learn the implicit priors of HSIs.
A Practical Contrastive Learning Framework for Single-Image Super-Resolution
Contrastive learning has achieved remarkable success on various high-level tasks, but there are fewer contrastive learning-based methods proposed for low-level tasks.
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data
In this paper, we propose a novel weakly-supervised framework to train a monocular depth estimation network to generate HR depth maps with resolution-mismatched supervision, i. e., the inputs are HR color images and the ground-truth are low-resolution (LR) depth maps.
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution
By this spectral splitting and aggregation strategy (SSAS), we can divide the original hyperspectral image into multiple samples (\emph{from less to more}) to support the efficient training of the network and effectively exploit the spectral correlations among spectrum.
Learning with Noisy Labels via Sparse Regularization
In this paper, we theoretically prove that \textbf{any loss can be made robust to noisy labels} by restricting the network output to the set of permutations over a fixed vector.
Physics-Based Iterative Projection Complex Neural Network for Phase Retrieval in Lensless Microscopy Imaging
Specifically, we unfold the iterative process of the alternative projection phase retrieval into a feed-forward neural network, whose layers mimic the processing flow.
Learning Scalable lY=-Constrained Near-Lossless Image Compression via Joint Lossy Image and Residual Compression
To achieve scalable compression with the error bound larger than zero, we derive the probability model of the quantized residual by quantizing the learned probability model of the original residual, instead of training multiple networks.
Asymmetric Loss Functions for Learning with Noisy Labels
Symmetric loss functions are confirmed to be robust to label noise.
BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring
In particular, we estimate the blur amounts of different regions by the internal geometric constraint of the DP data, which measures the defocus disparity between the left and right views.
Ranked #7 on
Image Defocus Deblurring
on DPD (Dual-view)
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference.
High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion
Specifically, to effectively extract and combine relevant information from LR depth and HR guidance, we propose a multi-modal attention based fusion (MMAF) strategy for hierarchical convolutional layers, including a feature enhance block to select valuable features and a feature recalibration block to unify the similarity metrics of modalities with different appearance characteristics.
Learning Scalable $\ell_\infty$-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression
To achieve scalable compression with the error bound larger than zero, we derive the probability model of the quantized residual by quantizing the learned probability model of the original residual, instead of training multiple networks.
Deep Learning-based Face Super-Resolution: A Survey
Second, we elaborate on the facial characteristics and popular datasets used in FSR.
Fully Unsupervised Person Re-identification viaSelective Contrastive Learning
In this work, we propose a novel selective contrastive learning framework for unsupervised feature learning.
When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach
Conventionally, image denoising and high-level vision tasks are handled separately in computer vision.
Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation
Learning from weakly-supervised data is one of the main challenges in machine learning and computer vision, especially for tasks such as image semantic segmentation where labeling is extremely expensive and subjective.
Random Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images
In this paper, we combine three image priors---Laplacian prior for DCT coefficients, sparsity prior and graph-signal smoothness prior for image patches---to construct an efficient JPEG soft decoding algorithm.
Data-Driven Sparsity-Based Restoration of JPEG-Compressed Images in Dual Transform-Pixel Domain
Arguably the most common cause of image degradation is compression.
