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Found 52 papers, 34 papers with code
Towards Realistic Low-Light Image Enhancement via ISP Driven Data Modeling
This broadens the degradation space and enhances the diversity of the training data, enabling the generated data to capture a wide range of degradations and the complexities inherent in the ISP pipeline.
Recurrent Feature Mining and Keypoint Mixup Padding for Category-Agnostic Pose Estimation
Hence, these works neglect to mine fine-grained and structure-aware (FGSA) features from both support and query images, which are crucial for pixel-level keypoint localization.
Diffusion-based Facial Aesthetics Enhancement with 3D Structure Guidance
To enhance facial aesthetics with less loss of identity, we propose the Nearest Neighbor Structure Guidance based on Diffusion (NNSG-Diffusion), a diffusion-based FAE method that beautifies a 2D facial image with 3D structure guidance.
Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Flexible and Effective Paradigm
Most of existing blind omnidirectional image quality assessment (BOIQA) models rely on viewport generation by modeling user viewing behavior or transforming omnidirectional images (OIs) into varying formats; however, these methods are either computationally expensive or less scalable.
Computational Analysis of Degradation Modeling in Blind Panoramic Image Quality Assessment
In this paper, we present a sufficient computational analysis of degradation modeling in BPIQA to thoroughly explore the \textit{easy-database issue}, where we carefully design three types of experiments via investigating the gap between BPIQA and blind image quality assessment (BIQA), the necessity of specific design in BPIQA models, and the generalization ability of BPIQA models.
Max360IQ: Blind Omnidirectional Image Quality Assessment with Multi-axis Attention
In this paper, we propose a novel and effective blind omnidirectional image quality assessment (BOIQA) model with multi-axis attention (Max360IQ), which can proficiently measure not only the quality of uniformly distorted omnidirectional images but also the quality of non-uniformly distorted omnidirectional images.
Omnidirectional Image Quality Captioning: A Large-scale Database and A New Model
The fast growing application of omnidirectional images calls for effective approaches for omnidirectional image quality assessment (OIQA).
Detecting Malicious Concepts Without Image Generation in AIGC
The platform needs a quick method to determine whether a concept is malicious to prevent the spread of malicious concepts.
Multitask Auxiliary Network for Perceptual Quality Assessment of Non-Uniformly Distorted Omnidirectional Images
The proposed network mainly consists of three parts: a backbone for extracting multiscale features from the viewport sequence, a multitask feature selection module for dynamically allocating specific features to different tasks, and auxiliary sub-networks for guiding the proposed model to capture local distortion and global quality change.
Subjective and Objective Quality Assessment of Non-Uniformly Distorted Omnidirectional Images
Omnidirectional image quality assessment (OIQA) has been one of the hot topics in IQA with the continuous development of VR techniques, and achieved much success in the past few years.
PSReg: Prior-guided Sparse Mixture of Experts for Point Cloud Registration
To solve this problem, we propose a prior-guided SMoE-based registration method to improve the feature distinctiveness by dispatching the potential correspondences to the same experts.
Video Quality Assessment for Online Processing: From Spatial to Temporal Sampling
Considering the fact that videos have highly redundant information, this paper investigates this problem from the perspective of joint spatial and temporal sampling, aiming to seek the answer to how little information we should keep at least when feeding videos into the VQA models while with acceptable performance sacrifice.
CD-NGP: A Fast Scalable Continual Representation for Dynamic Scenes
The experimental results on our long video sequences dataset show the superior scalability and reconstruction quality compared to existing state-of-the-art approaches.
GSTran: Joint Geometric and Semantic Coherence for Point Cloud Segmentation
The proposed network mainly consists of two principal components: a local geometric transformer and a global semantic transformer.
Lesion-aware network for diabetic retinopathy diagnosis
To this end, we propose a CNN-based DR diagnosis network with attention mechanism involved, termed lesion-aware network, to better capture lesion information from imbalanced data.
Multiscale Sliced Wasserstein Distances as Perceptual Color Difference Measures
Contemporary color difference (CD) measures for photographic images typically operate by comparing co-located pixels, patches in a ``perceptually uniform'' color space, or features in a learned latent space.
Arbitrary-Scale Video Super-Resolution with Structural and Textural Priors
Arbitrary-scale video super-resolution (AVSR) aims to enhance the resolution of video frames, potentially at various scaling factors, which presents several challenges regarding spatial detail reproduction, temporal consistency, and computational complexity.
Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal Dependency
Secondly, we propose a post-training stage that enables the model to reconstruct missing modalities in the prediction results when only partial modalities are available.
Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare
Extensive experiments on nine IQA datasets validate that the Compare2Score effectively bridges text-defined comparative levels during training with converted single image quality score for inference, surpassing state-of-the-art IQA models across diverse scenarios.
Meta-Point Learning and Refining for Category-Agnostic Pose Estimation
Existing methods only rely on the features extracted at support keypoints to predict or refine the keypoints on query image, but a few support feature vectors are local and inadequate for CAPE.
2AFC Prompting of Large Multimodal Models for Image Quality Assessment
While abundant research has been conducted on improving high-level visual understanding and reasoning capabilities of large multimodal models~(LMMs), their visual quality assessment~(IQA) ability has been relatively under-explored.
Perceptual Quality Assessment of 360$^\circ$ Images Based on Generative Scanpath Representation
To address these issues, we introduce a unique generative scanpath representation (GSR) for effective quality inference of 360$^\circ$ images, which aggregates varied perceptual experiences of multi-hypothesis users under a predefined viewing condition.
Integrating Large Pre-trained Models into Multimodal Named Entity Recognition with Evidential Fusion
Multimodal Named Entity Recognition (MNER) is a crucial task for information extraction from social media platforms such as Twitter.
Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation
Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the performance of novel classes while neglecting the performance of base classes.
ScanDMM: A Deep Markov Model of Scanpath Prediction for 360deg Images
Scanpath prediction for 360deg images aims to produce dynamic gaze behaviors based on the human visual perception mechanism.
CFNet: Conditional Filter Learning with Dynamic Noise Estimation for Real Image Denoising
A mainstream type of the state of the arts (SOTAs) based on convolutional neural network (CNN) for real image denoising contains two sub-problems, i. e., noise estimation and non-blind denoising.
GMF: General Multimodal Fusion Framework for Correspondence Outlier Rejection
In this paper, we propose General Multimodal Fusion (GMF) to learn to reject the correspondence outliers by leveraging both the structure and texture information.
A Novel Long-term Iterative Mining Scheme for Video Salient Object Detection
The existing state-of-the-art (SOTA) video salient object detection (VSOD) models have widely followed short-term methodology, which dynamically determines the balance between spatial and temporal saliency fusion by solely considering the current consecutive limited frames.
A Perceptually Optimized and Self-Calibrated Tone Mapping Operator
In Stage two, the input HDR image is self-calibrated to compute the final LDR image.
Perceptual Quality Assessment of Virtual Reality Videos in the Wild
Investigating how people perceive virtual reality (VR) videos in the wild (i. e., those captured by everyday users) is a crucial and challenging task in VR-related applications due to complex authentic distortions localized in space and time.
Distilling Knowledge from Object Classification to Aesthetics Assessment
To deal with this dilemma, we propose to distill knowledge on semantic patterns for a vast variety of image contents from multiple pre-trained object classification (POC) models to an IAA model.
Measuring Perceptual Color Differences of Smartphone Photographs
Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography.
IMFNet: Interpretable Multimodal Fusion for Point Cloud Registration
In this paper, we propose a new multimodal fusion method to generate a point cloud registration descriptor by considering both structure and texture information.
Ranked #1 on
Point Cloud Registration
on 3DMatch Benchmark
(using extra training data)
Image Quality Assessment in the Modern Age
This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA).
Perceptually Optimized Deep High-Dynamic-Range Image Tone Mapping
We describe a deep high-dynamic-range (HDR) image tone mapping operator that is computationally efficient and perceptually optimized.
Anomaly Detection in Video Sequences: A Benchmark and Computational Model
2) It provides the annotation data, including video-level labels (abnormal/normal video, anomaly type) and frame-level labels (abnormal/normal video frame) to facilitate anomaly detection.
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning
Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration.
Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition
A natural question then arises: Does the superior performance on the closed (and frequently re-used) test sets transfer to the open visual world with unconstrained variations?
Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images
Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains an open and challenging problem.
A multi-level approach with visual information for encrypted H.265/HEVC videos
High-efficiency video coding (HEVC) encryption has been proposed to encrypt syntax elements for the purpose of video encryption.
A Novel Video Salient Object Detection Method via Semi-supervised Motion Quality Perception
Consequently, we can achieve a significant performance improvement by using this new training set to start a new round of network training.
Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection
Existing RGB-D salient object detection methods treat depth information as an independent component to complement its RGB part, and widely follow the bi-stream parallel network architecture.
Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection
In this way, even though the overall video saliency quality is heavily dependent on its spatial branch, however, the performance of the temporal branch still matter.
Perceptual Quality Assessment of Smartphone Photography
As smartphones become people's primary cameras to take photos, the quality of their cameras and the associated computational photography modules has become a de facto standard in evaluating and ranking smartphones in the consumer market.
Ranked #4 on
Image Quality Assessment
on MSU NR VQA Database
Perceptual Quality Assessment of Omnidirectional Images as Moving Camera Videos
We first carry out a psychophysical experiment to investigate the interplay among the VR viewing conditions, the user viewing behaviors, and the perceived quality of 360{\deg} images.
Single Image Deraining: From Model-Based to Data-Driven and Beyond
The goal of single-image deraining is to restore the rain-free background scenes of an image degraded by rain streaks and rain accumulation.
Salient object detection on hyperspectral images using features learned from unsupervised segmentation task
Various saliency detection algorithms from color images have been proposed to mimic eye fixation or attentive object detection response of human observers for the same scenes.
Robustness Analysis of Pedestrian Detectors for Surveillance
In this study, we conduct the research on the robustness of pedestrian detection algorithms to video quality degradation.
Video Saliency Detection by 3D Convolutional Neural Networks
Different from salient object detection methods for still images, a key challenging for video saliency detection is how to extract and combine spatial and temporal features.
An Integration of Bottom-up and Top-Down Salient Cues on RGB-D Data: Saliency from Objectness vs. Non-Objectness
Bottom-up and top-down visual cues are two types of information that helps the visual saliency models.
Hyperspectral Image Dataset for Benchmarking on Salient Object Detection
Many works have been done on salient object detection using supervised or unsupervised approaches on colour images.
Saliency Detection by Forward and Backward Cues in Deep-CNNs
As prior knowledge of objects or object features helps us make relations for similar objects on attentional tasks, pre-trained deep convolutional neural networks (CNNs) can be used to detect salient objects on images regardless of the object class is in the network knowledge or not.
