Search Results for author:
Found 56 papers, 12 papers with code
Collaborative World Models: An Online-Offline Transfer RL Approach
Training visual reinforcement learning (RL) models in offline datasets is challenging due to overfitting issues in representation learning and overestimation problems in value function.
Large Language Models can be Guided to Evade AI-Generated Text Detection
Large Language Models (LLMs) have demonstrated exceptional performance in a variety of tasks, including essay writing and question answering.
Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library
Java is very powerful, but in Deep Learning field, its capabilities probably has not been sufficiently exploited.
Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems
Such attribute combinations are substantial clues to the underlying root causes and thus are called root causes of multidimensional data.
Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction
Multi-modal Magnetic Resonance Imaging (MRI) plays an important role in clinical medicine.
A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems
A stochastic-gradient-based interior-point algorithm for minimizing a continuously differentiable objective function (that may be nonconvex) subject to bound constraints is presented, analyzed, and demonstrated through experimental results.
Improving Urban Flood Prediction using LSTM-DeepLabv3+ and Bayesian Optimization with Spatiotemporal feature fusion
This study presented a CNN-RNN hybrid feature fusion modelling approach for urban flood prediction, which integrated the strengths of CNNs in processing spatial features and RNNs in analyzing different dimensions of time sequences.
Regularized Shallow Image Prior for Electrical Impedance Tomography
The results suggest that combining the shallow image prior and the hand-crafted regularization can achieve similar performance to the Deep Image Prior (DIP) but with less architectural dependency and complexity of the neural network.
Boundary-to-Solution Mapping for Groundwater Flows in a Toth Basin
In this paper, the authors propose a new approach to solving the groundwater flow equation in the Toth basin of arbitrary top and bottom topographies using deep learning.
A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging
Current SISR methods for 3D volumetric images are based on Generative Adversarial Networks (GANs), especially Wasserstein GANs due to their training stability.
A Spatio-temporal Decomposition Method for the Coordinated Economic Dispatch of Integrated Transmission and Distribution Grids
In the spatial dimension, a multi-parametric programming projection based spatial decomposition algorithm is developed to coordinate the ED problems of TG and DNs in a distributed manner.
Improving Video Retrieval by Adaptive Margin
While most video retrieval methods overlook that phenomenon, we propose an adaptive margin changed with the distance between positive and negative pairs to solve the aforementioned issue.
Anomaly Detection of UAV State Data Based on Single-class Triangular Global Alignment Kernel Extreme Learning Machine
To improve the performance of OCKELM, we choose a Triangular Global Alignment Kernel (TGAK) instead of an RBF Kernel and introduce the Fast Independent Component Analysis (FastICA) algorithm to reconstruct UAV data.
Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend
Intuitively, this finding suggests a natural way to improve model robustness by training the model on the $n$-FD examples.
RELIANT: Fair Knowledge Distillation for Graph Neural Networks
Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i. e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i. e., the teacher GNN model).
Towards Stable Human Pose Estimation via Cross-View Fusion and Foot Stabilization
Then the optimization-based method is introduced to reconstruct the foot pose and foot-ground contact for the general multi-view datasets including AIST++ and Human3. 6M.
Window Normalization: Enhancing Point Cloud Understanding by Unifying Inconsistent Point Densities
Existing methods are limited by the inconsistent point densities of different parts in the point cloud.
Ranked #2 on
Semantic Segmentation
on S3DIS
Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects
The mainstream crowd counting methods regress density map and integrate it to obtain counting results.
Growing Instance Mask on Leaf
Considering the superiorities above, we propose VeinMask to formulate the instance segmentation problem as the simulation of the vein growth process and to predict the major and minor veins in polar coordinates.
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices.
MAFNet: A Multi-Attention Fusion Network for RGB-T Crowd Counting
RGB-Thermal (RGB-T) crowd counting is a challenging task, which uses thermal images as complementary information to RGB images to deal with the decreased performance of unimodal RGB-based methods in scenes with low-illumination or similar backgrounds.
Physics-informed Deep Super-resolution for Spatiotemporal Data
High-fidelity simulation of complex physical systems is exorbitantly expensive and inaccessible across spatiotemporal scales.
SHDM-NET: Heat Map Detail Guidance with Image Matting for Industrial Weld Semantic Segmentation Network
This paper proposes an industrial weld segmentation network based on a deep learning semantic segmentation algorithm fused with heatmap detail guidance and Image Matting to solve the automatic segmentation problem of weld regions.
Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehicles
Trajectory prediction and behavioral decision-making are two important tasks for autonomous vehicles that require good understanding of the environmental context; behavioral decisions are better made by referring to the outputs of trajectory predictions.
Ranked #46 on
Motion Forecasting
on Argoverse CVPR 2020
Crowd Localization from Gaussian Mixture Scoped Knowledge and Scoped Teacher
The core reason of intrinsic scale shift being one of the most essential issues in crowd localization is that it is ubiquitous in crowd scenes and makes scale distribution chaotic.
Optimization-based Block Coordinate Gradient Coding for Mitigating Partial Stragglers in Distributed Learning
To reduce computational complexity, we first transform each to an equivalent but much simpler discrete problem with N\llL variables representing the partition of the L coordinates into N blocks, each with identical redundancy.
SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction
We also develop an edge-based atom-pair feature aggregation method to represent complex interactions and a graph pooling-based method to predict the binding affinity of the complex.
From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data
To be more specific, a language-guided SPCL method with a soft weighting strategy is explored in this work.
Dual-Stage Approach Toward Hyperspectral Image Super-Resolution
Hyperspectral image produces high spectral resolution at the sacrifice of spatial resolution.
DR.VIC: Decomposition and Reasoning for Video Individual Counting
Instead of relying on the Multiple Object Tracking (MOT) techniques, we propose to solve the problem by decomposing all pedestrians into the initial pedestrians who existed in the first frame and the new pedestrians with separate identities in each following frame.
A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients
We develop a deep learning approach to predicting a set of ventilator parameters for a mechanically ventilated septic patient using a long and short term memory (LSTM) recurrent neural network (RNN) model.
Towards Integrated Sensing and Communications for 6G
For the next generation of mobile communications systems, the integration of sensing and communications promises benefits in terms of spectrum utilization, cost, latency, area and weight.
THE Benchmark: Transferable Representation Learning for Monocular Height Estimation
Next, two new experimental protocols, zero-shot and few-shot cross-dataset transfer, are designed.
ForgeryNet -- Face Forgery Analysis Challenge 2021: Methods and Results
The rapid progress of photorealistic synthesis techniques has reached a critical point where the boundary between real and manipulated images starts to blur.
Adaptive Shrink-Mask for Text Detection
It weakens the coupling of texts to shrink-masks, which improves the robustness of detection results.
ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition
Discriminative local theme-level and object-level representations can be selected with the DLFS module from the spatially-correlated multi-modal RGB-D features.
LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds
The rapid development in visual crowd analysis shows a trend to count people by positioning or even detecting, rather than simply summing a density map.
Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification
In addition, due to domain gaps between different datasets, the performance is dramatically decreased when re-ID models pre-trained on label-rich datasets (source domain) are directly applied to other unlabeled datasets (target domain).
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.
Encoding physics to learn reaction-diffusion processes
Modeling complex spatiotemporal dynamical systems, such as the reaction-diffusion processes, have largely relied on partial differential equations (PDEs).
Waveform Design for Joint Sensing and Communications in Millimeter-Wave and Low Terahertz Bands
Finally, a data-embedded MS-QP (DE-MS-QP) waveform is constructed through time-domain extension of the MS-QP sequence, generating null frequency points on each subband for data transmission.
MT: Multi-Perspective Feature Learning Network for Scene Text Detection
Text detection, the key technology for understanding scene text, has become an attractive research topic.
Spatial-Spectral Clustering with Anchor Graph for Hyperspectral Image
In order to better handle the high dimensionality problem and preserve the spatial structures, this paper proposes a novel unsupervised approach called spatial-spectral clustering with anchor graph (SSCAG) for HSI data clustering.
BiP-Net: Bidirectional Perspective Strategy based Arbitrary-Shaped Text Detection Network
Specifically, a new text representation strategy is proposed to represent text contours from a top-down perspective, which can fit highly curved text contours effectively.
Auto-weighted Multi-view Feature Selection with Graph Optimization
In this paper, we focus on the unsupervised multi-view feature selection which tries to handle high dimensional data in the field of multi-view learning.
Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning
Graph-based semi-supervised learning methods, which deal well with the situation of limited labeled data, have shown dominant performance in practical applications.
Multi-channel Deep Supervision for Crowd Counting
To obtain the accurate supervision information of different channels, the MDSNet employs an auxiliary network called SupervisionNet (SN) to generate abundant supervision maps based on existing groundtruth.
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models
Reinforcement learning is a promising paradigm for solving sequential decision-making problems, but low data efficiency and weak generalization across tasks are bottlenecks in real-world applications.
Dynamics of a predator-prey system in open advective heterogeneous environments
In this paper, we investigate the effect of dispersal and advection on the dynamics of a predator-prey model.
Analysis of PDEs 35B35, 35K57, 92D25, 92D40
Inverse-design magnonic devices
The field of magnonics offers a new type of low-power information processing, in which magnons, the quanta of spin waves, carry and process data instead of electrons.
Applied Physics
Learning Independent Instance Maps for Crowd Localization
Furthermore, to improve the segmentation quality for different density regions, we present a differentiable Binarization Module (BM) to output structured instance maps.
Knowledge-Enhanced Named Entity Disambiguation for Short Text
Named entity disambiguation is an important task that plays the role of bridge between text and knowledge.
CM-Net: Concentric Mask based Arbitrary-Shaped Text Detection
Extensive experiments demonstrate the proposed CM is efficient and robust to fit arbitrary-shaped text instances, and also validate the effectiveness of MPF and constraints loss for discriminative text features recognition.
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
In this paper, we combine both to propose an Unsupervised Semantic Aggregation and Deformable Template Matching (USADTM) framework for SSL, which strives to improve the classification performance with few labeled data and then reduce the cost in data annotating.
Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance
To reduce the gap, in this paper, we propose a domain-adaptation-style crowd counting method, which can effectively adapt the model from synthetic data to the specific real-world scenes.
QMR:Q-learning based Multi-objective optimization Routing protocol for Flying Ad Hoc Networks
It is difficult for existing routing protocols for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs) to adapt the high dynamics of FANETs.
