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Found 45 papers, 29 papers with code
Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videos
Traffic anomaly detection (TAD) in driving videos is critical for ensuring the safety of autonomous driving and advanced driver assistance systems.
Hyperspectral Image Denoising via Spatial-Spectral Recurrent Transformer
This block consists of a spatial branch and a spectral branch.
Progressive Poisoned Data Isolation for Training-time Backdoor Defense
Extensive experiments on multiple benchmark datasets and DNN models, assessed against nine state-of-the-art backdoor attacks, demonstrate the superior performance of our PIPD method for backdoor defense.
Image Demoireing in RAW and sRGB Domains
Moire patterns frequently appear when capturing screens with smartphones or cameras, potentially compromising image quality.
SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models
Both quantitative and qualitative results on this evaluation dataset indicate that our SmartEdit surpasses previous methods, paving the way for the practical application of complex instruction-based image editing.
Regroup Median Loss for Combating Label Noise
The deep model training procedure requires large-scale datasets of annotated data.
Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examples
Extensive experiments demonstrate that 1) the classification accuracy of the classifier trained in the plaintext domain remains the same in both the ciphertext and plaintext domains; 2) the encrypted images can be recovered into their original form with an average PSNR of up to 51+ dB for the SVHN dataset and 48+ dB for the VGGFace2 dataset; 3) our system exhibits satisfactory generalization capability on the encryption, decryption and classification tasks across datasets that are different from the training one; and 4) a high-level of security is achieved against three potential threat models.
Generating Robust Adversarial Examples against Online Social Networks (OSNs)
In this work, we aim to design a new framework for generating robust AEs that can survive the OSN transmission; namely, the AEs before and after the OSN transmission both possess strong attack capabilities.
A Comparative Study of Image Restoration Networks for General Backbone Network Design
Following this, we present the benchmark results and analyze the reasons behind the performance disparity of different models across various tasks.
Unifying Image Processing as Visual Prompting Question Answering
To address this issue, we propose a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks, etc.
HAT: Hybrid Attention Transformer for Image Restoration
In the training stage, we additionally adopt a same-task pre-training strategy to further exploit the potential of the model for further improvement.
Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation
However, the majority of available resources are still in standard dynamic range (SDR).
CNN Injected Transformer for Image Exposure Correction
Capturing images with incorrect exposure settings fails to deliver a satisfactory visual experience.
Direction-aware Video Demoireing with Temporal-guided Bilateral Learning
In TDR, we propose a temporal-guided bilateral learning pipeline to mitigate the degradation of color and details caused by the moire patterns while preserving the restored frequency information in FDDA.
Rethinking Image Forgery Detection via Contrastive Learning and Unsupervised Clustering
To resolve this dilemma, we propose the FOrensic ContrAstive cLustering (FOCAL) method, a novel, simple yet very effective paradigm based on contrastive learning and unsupervised clustering for the image forgery detection.
Under-Display Camera Image Restoration with Scattering Effect
With the physical model of the scattering effect, we improve the image formation pipeline for the image synthesis to construct a realistic UDC dataset with ground truths.
A Memory-Augmented Multi-Task Collaborative Framework for Unsupervised Traffic Accident Detection in Driving Videos
Different from previous approaches, our method can more accurately detect both ego-involved and non-ego accidents by simultaneously modeling appearance changes and object motions in video frames through the collaboration of optical flow reconstruction and future object localization tasks.
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models
After detecting the artifact regions, we develop a finetune procedure to improve GAN-based SR models with a few samples, so that they can deal with similar types of artifacts in more unseen real data.
Common Knowledge Learning for Generating Transferable Adversarial Examples
Specifically, to reduce the model-specific features and obtain better output distributions, we construct a multi-teacher framework, where the knowledge is distilled from different teacher architectures into one student network.
Generalizable Synthetic Image Detection via Language-guided Contrastive Learning
In this work, we propose a simple yet very effective synthetic image detection method via a language-guided contrastive learning and a new formulation of the detection problem.
STGlow: A Flow-based Generative Framework with Dual Graphormer for Pedestrian Trajectory Prediction
Different from previous approaches, our method can more precisely model the underlying data distribution by optimizing the exact log-likelihood of motion behaviors.
Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution
Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction.
Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolution
Then, we incorporate the proposed feature embedding scheme into a source-consistent super-resolution framework that is physically-interpretable, producing lightweight PDE-Net, in which high-resolution (HR) HS images are iteratively refined from the residuals between input low-resolution (LR) HS images and pseudo-LR-HS images degenerated from reconstructed HR-HS images via probability-inspired HS embedding.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
Activating More Pixels in Image Super-Resolution Transformer
In the training stage, we additionally adopt a same-task pre-training strategy to exploit the potential of the model for further improvement.
Ranked #1 on
Image Super-Resolution
on Set5 - 2x upscaling
Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities
Specifically, we design a tag encoding module to cover both the single modality and multiple modalities missing cases, so as to guide the network's attention to those missing modalities.
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing
Furthermore, we present a unified framework for incorporating various GSR and LR models and discuss the relationship between GSR and LR models.
A Principled Design of Image Representation: Towards Forensic Tasks
Image forensics is a rising topic as the trustworthy multimedia content is critical for modern society.
Robust Image Forgery Detection Over Online Social Network Shared Images
To fight against the OSN-shared forgeries, in this work, a novel robust training scheme is proposed.
Probabilistic Selective Encryption of Convolutional Neural Networks for Hierarchical Services
Model protection is vital when deploying Convolutional Neural Networks (CNNs) for commercial services, due to the massive costs of training them.
Self-Supervised Adversarial Example Detection by Disentangled Representation
To alleviate this problem, we explore how to detect adversarial examples with disentangled label/semantic features under the autoencoder structure.
A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation
Image representation is an important topic in computer vision and pattern recognition.
Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domain
Deep neural networks (DNNs) have been shown to be vulnerable against adversarial examples (AEs), which are maliciously designed to cause dramatic model output errors.
GIID-Net: Generalizable Image Inpainting Detection via Neural Architecture Search and Attention
The proposed GIID-Net consists of three sub-blocks: the enhancement block, the extraction block and the decision block.
Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-Supervision
Predicting human motion behavior in a crowd is important for many applications, ranging from the natural navigation of autonomous vehicles to intelligent security systems of video surveillance.
Ranked #15 on
Trajectory Prediction
on ETH/UCY
SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising
This model first projects the observed HSIs into a low-dimensional orthogonal subspace, and then represents the projected image with a multidimensional dictionary.
Deep Generative Model for Image Inpainting with Local Binary Pattern Learning and Spatial Attention
Deep learning (DL) has demonstrated its powerful capabilities in the field of image inpainting.
Privacy Leakage of SIFT Features via Deep Generative Model based Image Reconstruction
It is shown that, if the adversary can only have access to the SIFT descriptors while not their coordinates, then the modest success of reconstructing the latent image can be achieved for highly-structured images (e. g., faces) and would fail in general settings.
Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning
Specifically, PZRes-Net learns a high resolution and \textit{zero-centric} residual image, which contains high-frequency spatial details of the scene across all spectral bands, from both inputs in a progressive fashion along the spectral dimension.
Hyperspectral Image Super-Resolution
Hyperspectral Unmixing
+1
The Power of Triply Complementary Priors for Image Compressive Sensing
In this paper, we propose a joint low-rank and deep (LRD) image model, which contains a pair of triply complementary priors, namely \textit{external} and \textit{internal}, \textit{deep} and \textit{shallow}, and \textit{local} and \textit{non-local} priors.
Toward Better Understanding of Saliency Prediction in Augmented 360 Degree Videos
The saliency annotations of head and eye movements for both original and augmented videos are collected and together constitute the ARVR dataset.
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration
Towards this end, we first obtain a good reference of the original image groups by using the image NSS prior, and then the rank residual of the image groups between this reference and the degraded image is minimized to achieve a better estimate to the desired image.
A Benchmark for Sparse Coding: When Group Sparsity Meets Rank Minimization
Sparse coding has achieved a great success in various image processing tasks.
A Comparative Study for the Nuclear Norms Minimization Methods
Inspired by enhancing sparsity of the weighted L1-norm minimization in comparison with L1-norm minimization in sparse representation, we thus explain that WNNM is more effective than NMM.
Data-Driven Sparsity-Based Restoration of JPEG-Compressed Images in Dual Transform-Pixel Domain
Arguably the most common cause of image degradation is compression.
