Search Results for author:
Found 53 papers, 12 papers with code
THQA: A Perceptual Quality Assessment Database for Talking Heads
In the realm of media technology, digital humans have gained prominence due to rapid advancements in computer technology.
Social Physics Informed Diffusion Model for Crowd Simulation
In this paper, we propose a social physics-informed diffusion model named SPDiff to mitigate the above gap.
Few-Shot Class-Incremental Learning with Prior Knowledge
To tackle the issues of catastrophic forgetting and overfitting in few-shot class-incremental learning (FSCIL), previous work has primarily concentrated on preserving the memory of old knowledge during the incremental phase.
Growing from Exploration: A self-exploring framework for robots based on foundation models
In this work, we propose a framework named GExp, which enables robots to explore and learn autonomously without human intervention.
Uncertainty-aware Sampling for Long-tailed Semi-supervised Learning
This approach allows the model to perceive the uncertainty of pseudo-labels at different training stages, thereby adaptively adjusting the selection thresholds for different classes.
Federated PAC-Bayesian Learning on Non-IID data
Existing research has either adapted the Probably Approximately Correct (PAC) Bayesian framework for federated learning (FL) or used information-theoretic PAC-Bayesian bounds while introducing their theorems, but few considering the non-IID challenges in FL.
Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer
To bridge these gaps, in this paper, we propose Tem-Adapter, which enables the learning of temporal dynamics and complex semantics by a visual Temporal Aligner and a textual Semantic Aligner.
Ranked #1 on
Video Question Answering
on SUTD-TrafficQA
Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning for Resource Management in Space-Air-Ground Integrated Networks
The Space-Air-Ground Integrated Network (SAGIN), integrating heterogeneous devices including low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users (GUs), holds significant promise for advancing smart city applications.
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy
In response, this paper introduces AQUILA (adaptive quantization in device selection strategy), a novel adaptive framework devised to effectively handle these issues, enhancing the efficiency and robustness of FL.
Blind Image Quality Assessment: A Fuzzy Neural Network for Opinion Score Distribution Prediction
On the other hand, we also prove the feasibility of the proposed method in predicting the MOS of image quality on several popular IQA databases, including CSIQ, TID2013, LIVE MD, and LIVE Challenge.
Language-free Compositional Action Generation via Decoupling Refinement
Composing simple elements into complex concepts is crucial yet challenging, especially for 3D action generation.
Don't worry about mistakes! Glass Segmentation Network via Mistake Correction
The IS is designed to simulate the detection procedure of human recognition for identifying transparent glass by global context and edge information.
Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning
Quantitative markets are characterized by swift dynamics and abundant uncertainties, making the pursuit of profit-driven stock trading actions inherently challenging.
Finding the Most Transferable Tasks for Brain Image Segmentation
Furthermore, within the same modality, transferring from the source task that has stronger RoI shape similarity with the target task can significantly improve the final transfer performance.
Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds
Here, a visual-tactile fusion framework for transparent object grasping under complex backgrounds and variant light conditions is proposed, including the grasping position detection, tactile calibration, and visual-tactile fusion based classification.
Segmented Learning for Class-of-Service Network Traffic Classification
The commonality of statistical features among the network flow segments motivates us to propose novel segmented learning that includes essential vector representation and a simple-segment method of classification.
Transferability-Guided Cross-Domain Cross-Task Transfer Learning
We propose two novel transferability metrics F-OTCE (Fast Optimal Transport based Conditional Entropy) and JC-OTCE (Joint Correspondence OTCE) to evaluate how much the source model (task) can benefit the learning of the target task and to learn more transferable representations for cross-domain cross-task transfer learning.
Dual Vision Transformer
Dual-ViT is henceforth able to reduce the computational complexity without compromising much accuracy.
Detail-recovery Image Deraining via Dual Sample-augmented Contrastive Learning
To bridge the two domain gaps, we propose a semi-supervised detail-recovery image deraining network (Semi-DRDNet) with dual sample-augmented contrastive learning.
SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications
It is demonstrated that SAFARI under unreliable communications is guaranteed to converge at the same rate as the standard FedAvg with perfect communications.
Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds
Differently, our network is designed to refine the initial normal of each point by extracting additional information from multiple feature representations.
Parameterized Image Quality Score Distribution Prediction
Experimental results verifythe feasibility of using alpha stable model to describe the IQSD, and prove the effectiveness of objective alpha stable model basedIQSD prediction method.
Sequential Doppler Shift based Optimal Localization and Synchronization with TOA
In this paper, we develop a new optimal LAS method in the TDBS, namely LAS-SDT, by taking advantage of the sequential Doppler shift and TOA measurements.
Boosting Video Representation Learning with Multi-Faceted Integration
Video content is multifaceted, consisting of objects, scenes, interactions or actions.
Closed-form Two-way TOA Localization and Synchronization for User Devices with Motion and Clock Drift
Numerical results in a 3D scenario verify the theoretical analysis that the estimation accuracy of the new CFTWLAS method reaches CRLB in the presented experiments when the number of the ANs is large, the geometry is appropriate, and the noise is small.
Time-Distributed Feature Learning in Network Traffic Classification for Internet of Things
The network traffic classification (NTC) is an essential tool to explore behaviours of network flows, and NTC is required for Internet service providers (ISPs) to manage the performance of the IoT network.
Virtual Reality Gaming on the Cloud: A Reality Check
To the best of the authors' knowledge, this is the first measurement study and analysis conducted using a commercial cloud VR gaming platform, and under both fixed and adaptive bitrate streaming.
Angel's Girl for Blind Painters: an Efficient Painting Navigation System Validated by Multimodal Evaluation Approach
This work demonstrates that it is practicable for the blind people to feel the world through the brush in their hands.
Direction-aware Feature-level Frequency Decomposition for Single Image Deraining
Second, we further establish communication channels between low-frequency maps and high-frequency maps to interactively capture structures from high-frequency maps and add them back to low-frequency maps and, simultaneously, extract details from low-frequency maps and send them back to high-frequency maps, thereby removing rain streaks while preserving more delicate features in the input image.
Exploiting Relationship for Complex-scene Image Generation
Our method tends to synthesize plausible layouts and objects, respecting the interplay of multiple objects in an image.
A New TOA Localization and Synchronization System with Virtually Synchronized Periodic Asymmetric Ranging Network
In this article, we design a new time-of-arrival (TOA) system for simultaneous user device (UD) localization and synchronization with a periodic asymmetric ranging network, namely PARN.
Non-local Channel Aggregation Network for Single Image Rain Removal
Second, aggregating channels could help our model to concentrate on channels more related to image background instead of rain streaks.
Semidefinite Programming Two-way TOA Localization for User Devices with Motion and Clock Drift
In two-way time-of-arrival (TOA) systems, a user device (UD) obtains its position by round-trip communications to a number of anchor nodes (ANs) at known locations.
Explainable Person Re-Identification with Attribute-guided Metric Distillation
In this paper, we propose a post-hoc method, named Attribute-guided Metric Distillation (AMD), to explain existing ReID models.
Ranked #44 on
Person Re-Identification
on DukeMTMC-reID
New Closed-form Joint Localization and Synchronization using Sequential One-way TOAs
Compared with the conventional iterative method, the proposed new CFJLAS method does not require initialization, obtains the optimal solution under the small noise condition, and has a low computational complexity.
Optimal Localization with Sequential Pseudorange Measurements for Moving Users in a Time Division Broadcast Positioning System
We show that the LSPM-D is a special case of the LSPM-KVD when the UD is stationary with zero velocity.
A Closed-form Localization Method Utilizing Pseudorange Measurements from Two Non-synchronized Positioning Systems
Numerical results verify the theoretical analysis on positioning accuracy, and show that the new CDL method has superior performance over the state-of-the-art closed-form method.
Optimal Two-way TOA Localization and Synchronization for Moving User Devices with Clock Drift
We show that the conventional two-way TOA method is a special case of the TWLAS when the UD is stationary, and the TWLAS has high estimation accuracy than the conventional one-way TOA method.
Claw U-Net: A Unet-based Network with Deep Feature Concatenation for Scleral Blood Vessel Segmentation
Sturge-Weber syndrome (SWS) is a vascular malformation disease, and it may cause blindness if the patient's condition is severe.
Identification of deep breath while moving forward based on multiple body regions and graph signal analysis
This paper presents an unobtrusive solution that can automatically identify deep breath when a person is walking past the global depth camera.
The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances
To start with, we present an overview of the end-to-end deep face recognition.
Change Detection in Heterogeneous Optical and SAR Remote Sensing Images via Deep Homogeneous Feature Fusion
To solve this problem, this paper presents a new homogeneous transformation model termed deep homogeneous feature fusion (DHFF) based on image style transfer (IST).
Dynamic Spatiotemporal Graph Neural Network with Tensor Network
Dynamic spatial graph construction is a challenge in graph neural network (GNN) for time series data problems.
Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner
During the epidemic prevention and control period, our study can be helpful in prognosis, diagnosis and screening for the patients infected with COVID-19 (the novel coronavirus) based on breathing characteristics.
Point cloud denoising based on tensor Tucker decomposition
In this paper, we propose a new algorithm for point cloud denoising based on the tensor Tucker decomposition.
Interactive Binary Image Segmentation with Edge Preservation
Binary image segmentation plays an important role in computer vision and has been widely used in many applications such as image and video editing, object extraction, and photo composition.
Multimodal Fusion via a Series of Transfers for Noise Removal
Different from conventional fusion approaches, the proposed method conducts a series of transfers: contrast, detail, and color transfers.
Near-Infrared Coloring via a Contrast-Preserving Mapping Model
This paper introduces a new coloring method to add colors to near-infrared gray images based on a contrast-preserving mapping model.
Rain Removal via Shrinkage-Based Sparse Coding and Learned Rain Dictionary
Based on this error map, both the sparse codes of rain and non-rain dictionaries are used jointly to represent the image structures of objects and avoid the edge artifacts in the non-rain regions.
Rain structure transfer using an exemplar rain image for synthetic rain image generation
Next, residual rain patches are selected randomly, and then added to the given target image along a raster scanning direction.
Near-Infrared Image Dehazing Via Color Regularization
It is also shown that the proposed color regularization can remove the edge artifacts which arise from the use of the conventional dark prior model.
A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation
We propose a weighted variational model to estimate both the reflectance and the illumination from an observed image.
Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI
The size of the dictionary and the patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables.
