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Found 38 papers, 17 papers with code
AOCIL: Exemplar-free Analytic Online Class Incremental Learning with Low Time and Resource Consumption
Online Class Incremental Learning (OCIL) aims to train the model in a task-by-task manner, where data arrive in mini-batches at a time while previous data are not accessible.
SinSR: Diffusion-Based Image Super-Resolution in a Single Step
Extensive experiments conducted on synthetic and real-world datasets demonstrate that the proposed method can achieve comparable or even superior performance compared to both previous SOTA methods and the teacher model, in just one sampling step, resulting in a remarkable up to x10 speedup for inference.
Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method
The past decade has witnessed great strides in video recovery by specialist technologies, like video inpainting, completion, and error concealment.
ExposureDiffusion: Learning to Expose for Low-light Image Enhancement
Different from a vanilla diffusion model that has to perform Gaussian denoising, with the injected physics-based exposure model, our restoration process can directly start from a noisy image instead of pure noise.
Ranked #1 on
Image Denoising
on Image Denoising on SID x300
Beyond Learned Metadata-based Raw Image Reconstruction
Besides, we propose a novel design of the context model, which can better predict the order masks of encoding/decoding based on both the sRGB image and the masks of already processed features.
A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking
Surprisingly, there has been a lack of comprehensive studies on the robustness of RS tasks, prompting us to undertake a thorough survey and benchmark on the robustness of image classification and object detection in RS.
A Byte Sequence is Worth an Image: CNN for File Fragment Classification Using Bit Shift and n-Gram Embeddings
File fragment classification (FFC) on small chunks of memory is essential in memory forensics and Internet security.
Bitstream-Corrupted JPEG Images are Restorable: Two-stage Compensation and Alignment Framework for Image Restoration
In this paper, we study a real-world JPEG image restoration problem with bit errors on the encrypted bitstream.
Raw Image Reconstruction with Learned Compact Metadata
While raw images exhibit advantages over sRGB images (e. g., linearity and fine-grained quantization level), they are not widely used by common users due to the large storage requirements.
PEM: Perception Error Model for Virtual Testing of Autonomous Vehicles
In this article, we define Perception Error Models (PEM), a virtual simulation component that can enable the analysis of the impact of perception errors on AV safety, without the need to model the sensors themselves.
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.
CoPEM: Cooperative Perception Error Models for Autonomous Driving
In this paper, we introduce the notion of Cooperative Perception Error Models (coPEMs) towards achieving an effective and efficient integration of V2X solutions within a virtual test environment.
TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting
Specifically, TAFNet is divided into one main stream and two auxiliary streams.
Variational Disentanglement for Domain Generalization
Domain generalization aims to learn an invariant model that can generalize well to the unseen target domain.
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
Weakly-supervised Part-Attention and Mentored Networks for Vehicle Re-Identification
Current part-level feature learning methods typically detect vehicle parts via uniform division, outside tools, or attention modeling.
Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restoration
A progressive texture blending module is designed to blend the encoded two-stream features in a multi-scale and progressive manner.
Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation
Though convolutional neural networks are widely used in different tasks, lack of generalization capability in the absence of sufficient and representative data is one of the challenges that hinder their practical application.
PDPGD: Primal-Dual Proximal Gradient Descent Adversarial Attack
In this work, we introduce a fast, general and accurate adversarial attack that optimises the original non-convex constrained minimisation problem.
Multi-object Tracking with Tracked Object Bounding Box Association
The CenterTrack tracking algorithm achieves state-of-the-art tracking performance using a simple detection model and single-frame spatial offsets to localize objects and predict their associations in a single network.
Moving Towards Centers: Re-ranking with Attention and Memory for Re-identification
Specifically, all the feature embeddings of query and gallery images are expanded and enhanced by a linear combination of their neighbors, with the correlation prediction serving as discriminative combination weights.
Dense Point Prediction: A Simple Baseline for Crowd Counting and Localization
In this paper, we propose a simple yet effective crowd counting and localization network named SCALNet.
Rethinking and Designing a High-performing Automatic License Plate Recognition Approach
Our study illustrates the outstanding design of ALPR with four insights: (1) the resampling-based cascaded framework is beneficial to both speed and accuracy; (2) the highly efficient license plate recognition should abundant additional character segmentation and recurrent neural network (RNN), but adopt a plain convolutional neural network (CNN); (3) in the case of CNN, taking advantage of vertex information on license plates improves the recognition performance; and (4) the weight-sharing character classifier addresses the lack of training images in small-scale datasets.
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.
A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds
In this paper, we propose a novel self-training approach named Crowd-SDNet that enables a typical object detector trained only with point-level annotations (i. e., objects are labeled with points) to estimate both the center points and sizes of crowded objects.
Deep Spatial-angular Regularization for Compressive Light Field Reconstruction over Coded Apertures
Coded aperture is a promising approach for capturing the 4-D light field (LF), in which the 4-D data are compressively modulated into 2-D coded measurements that are further decoded by reconstruction algorithms.
Image and Video Processing
When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks
However, the existing networks usually suffer from either redundancy of convolutional layers or insufficient utilization of parameters.
Convolutional Neural Networks with Dynamic Regularization
That is, the regularization strength is fixed to a predefined schedule, and manual adjustments are required to adapt to various network architectures.
Stratified Labeling for Surface Consistent Parallax Correction and Occlusion Completion
A stratified synthesis strategy is adopted which parses the scene content based on stratified disparity layers and across a varying range of spatial granularities.
Improved Network Robustness with Adversary Critic
Our main idea is: adversarial examples for the robust classifier should be indistinguishable from the regular data of the adversarial target.
Haze Density Estimation via Modeling of Scattering Coefficients of Iso-depth Regions
Vision based haze density estimation is of practical implications for the purpose of precaution alarm and emergency reactions toward disastrous hazy weathers.
Light Field Denoising via Anisotropic Parallax Analysis in a CNN Framework
Light field (LF) cameras provide perspective information of scenes by taking directional measurements of the focusing light rays.
Remote Detection of Idling Cars Using Infrared Imaging and Deep Networks
First, we detect the cars in each IR image using a convolutional neural network, which is pre-trained on regular RGB images and fine-tuned on IR images for higher accuracy.
Robust Video Content Alignment and Compensation for Clear Vision Through the Rain
Extensive evaluations show that advantage of up to 5dB is achieved on the scene restoration PSNR over state-of-the-art methods, and the advantage is especially obvious with highly complex and dynamic scenes.
Robust Video Content Alignment and Compensation for Rain Removal in a CNN Framework
Visual inspection shows that much cleaner rain removal is achieved especially for highly dynamic scenes with heavy and opaque rainfall from a fast moving camera.
Multimodal Recurrent Neural Networks with Information Transfer Layers for Indoor Scene Labeling
This paper proposes a new method called Multimodal RNNs for RGB-D scene semantic segmentation.
Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions
Significant improvements have been made in terms of overall depth estimation error; however, current state-of-the-art methods still show limitations in handling intricate occluding structures and complex scenes with multiple occlusions.
Light Field Compression with Disparity Guided Sparse Coding based on Structural Key Views
Recent imaging technologies are rapidly evolving for sampling richer and more immersive representations of the 3D world.
