Search Results for author:
Found 33 papers, 21 papers with code
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.
Anomaly Detection In Surveillance Videos
Multiple Instance Learning
+2
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.
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.
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.
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.
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.
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.
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.
