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Found 183 papers, 38 papers with code
Collaborative Learning of Gesture Recognition and 3D Hand Pose Estimation with Multi-Order Feature Analysis
The proposed network exploits joint-aware features that are crucial for both tasks, with which gesture recognition and 3D hand pose estimation boost each other to learn highly discriminative features and models.
OID: Outlier Identifying and Discarding in Blind Image Deblurring
Even a small amount of outliers can dramatically degrade the quality of the estimated blur kernel, because the outliers are not conforming to the linear formation of the blurring process.
HARD-Net: Hardness-AwaRe Discrimination Network for 3D Early Activity Prediction
In this paper, we propose a novel Hardness-AwaRe Discrimination Network (HARD-Net) to specifically investigate the relationships between the similar activity pairs that are hard to be discriminated.
Ranked #3 on
Skeleton Based Action Recognition
on UAV-Human
Learning Progressive Joint Propagation for Human Motion Prediction
Last, in order to incorporate a general motion space for high-quality prediction, we build a memory-based dictionary, which aims to preserve the global motion patterns in training data to guide the predictions.
Adaptive Computationally Efficient Network for Monocular 3D Hand Pose Estimation
A novel model, called Adaptive Computationally Efficient (ACE) network, is proposed, which takes advantage of a Gaussian kernel based Gate Module to dynamically switch the computation between a light model and a heavy network for feature extraction.
SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
Then we derive two specific attention modules, named SpatialFormer Semantic Attention (SFSA) and SpatialFormer Target Attention (SFTA), to enhance the target object regions while reduce the background distraction.
Precise Facial Landmark Detection by Reference Heatmap Transformer
Most facial landmark detection methods predict landmarks by mapping the input facial appearance features to landmark heatmaps and have achieved promising results.
Robustly Complete Finite-State Abstractions for Control Synthesis of Stochastic Systems
The essential step of abstraction-based control synthesis for nonlinear systems to satisfy a given specification is to obtain a finite-state abstraction of the original systems.
Almost Sure Saddle Avoidance of Stochastic Gradient Methods without the Bounded Gradient Assumption
We prove that various stochastic gradient descent methods, including the stochastic gradient descent (SGD), stochastic heavy-ball (SHB), and stochastic Nesterov's accelerated gradient (SNAG) methods, almost surely avoid any strict saddle manifold.
Bridging Physics-Informed Neural Networks with Reinforcement Learning: Hamilton-Jacobi-Bellman Proximal Policy Optimization (HJBPPO)
The Proximal Policy Optimization (PPO)-Clipped algorithm is improvised with this implementation as it uses a value network to compute the objective function for its policy network.
Modeling Uncertain Feature Representation for Domain Generalization
In this paper, we improve the network generalization ability by modeling domain shifts with uncertainty (DSU), i. e., characterizing the feature statistics as uncertain distributions during training.
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing.
STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition
For the first time, we introduce vision Transformers into PPAR by treating a video as a tubelet sequence, and accordingly design two complementary mechanisms, i. e., sparsification and anonymization, to remove privacy from a spatio-temporal perspective.
Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical Reasoning over Text
(2) How to enhance the perception of reasoning types for the models?
GPTR: Gestalt-Perception Transformer for Diagram Object Detection
These lead to the fact that traditional data-driven detection model is not suitable for diagrams.
HDNet: A Hierarchically Decoupled Network for Crowd Counting
Recently, density map regression-based methods have dominated in crowd counting owing to their excellent fitting ability on density distribution.
DiffPose: Toward More Reliable 3D Pose Estimation
Monocular 3D human pose estimation is quite challenging due to the inherent ambiguity and occlusion, which often lead to high uncertainty and indeterminacy.
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision
This paper builds a unified framework to perform effective noisy-proposal suppression and to interact between global and local features for robust feature learning.
tSF: Transformer-based Semantic Filter for Few-Shot Learning
Few-Shot Learning (FSL) alleviates the data shortage challenge via embedding discriminative target-aware features among plenty seen (base) and few unseen (novel) labeled samples.
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective
In this paper, for the first time, we investigate the contributions of different distance metrics, and propose an adaptive fusion scheme, bringing significant improvements in few-shot classification.
Improving the Reliability for Confidence Estimation
Confidence estimation, a task that aims to evaluate the trustworthiness of the model's prediction output during deployment, has received lots of research attention recently, due to its importance for the safe deployment of deep models.
Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior
To address this challenge, we suggest a dataset-free deep residual prior for the kernel induced error (termed as residual) expressed by a customized untrained deep neural network, which allows us to flexibly adapt to different blurs and images in real scenarios.
Heatmap Distribution Matching for Human Pose Estimation
In this paper, we show that optimizing the heatmap prediction in such a way, the model performance of body joint localization, which is the intrinsic objective of this task, may not be consistently improved during the optimization process of the heatmap prediction.
Downlink Compression Improves TopK Sparsification
One of the largest bottlenecks in distributed training is communicating gradients across different nodes.
Multiple Control Barrier Functions: An Application to Reactive Obstacle Avoidance for a Multi-steering Tractor-trailer System
We develop a control structure based on a multiple CBFs scheme for a multi-steering tractor-trailer system to ensure a collision-free maneuver for both the tractor and trailer in the presence of several obstacles.
nVFNet-RDC: Replay and Non-Local Distillation Collaboration for Continual Object Detection
Continual Learning (CL) focuses on developing algorithms with the ability to adapt to new environments and learn new skills.
Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances.
Ranked #2 on
Few-Shot Object Detection
on MS-COCO (1-shot)
Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition
We design a novel Dynamic Spatio-Temporal Specialization (DSTS) module, which consists of specialized neurons that are only activated for a subset of samples that are highly similar.
Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation
Second, to resist overfitting issues caused by few training samples, a hyper-class embedding is learned by clustering all category embeddings for initialization and aligned with category embedding of the new class for enhancement, where learned knowledge assists to learn new knowledge, thus alleviating performance dependence on training data scale.
IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition
Human interaction recognition is very important in many applications.
Meta Spatio-Temporal Debiasing for Video Scene Graph Generation
Video scene graph generation (VidSGG) aims to parse the video content into scene graphs, which involves modeling the spatio-temporal contextual information in the video.
ERA: Expert Retrieval and Assembly for Early Action Prediction
Early action prediction aims to successfully predict the class label of an action before it is completely performed.
Position-prior Clustering-based Self-attention Module for Knee Cartilage Segmentation
The morphological changes in knee cartilage (especially femoral and tibial cartilages) are closely related to the progression of knee osteoarthritis, which is expressed by magnetic resonance (MR) images and assessed on the cartilage segmentation results.
Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees
This paper proposes a learning framework to simultaneously stabilize an unknown nonlinear system with a neural controller and learn a neural Lyapunov function to certify a region of attraction (ROA) for the closed-loop system.
Data-Driven Learning of Safety-Critical Control with Stochastic Control Barrier Functions
More specifically, we propose a data-driven stochastic control barrier function (DDSCBF) framework and use supervised learning to learn the unknown stochastic dynamics via the DDSCBF scheme.
GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation
However, existing graph-based methods fails to consider the bias offsets of users (items).
Surface Representation for Point Clouds
Based on a simple baseline of PointNet++ (SSG version), Umbrella RepSurf surpasses the previous state-of-the-art by a large margin for classification, segmentation and detection on various benchmarks in terms of performance and efficiency.
Ranked #2 on
3D Point Cloud Classification
on ModelNet40
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention
Visual features of skin lesions vary significantly because the images are collected from patients with different lesion colours and morphologies by using dissimilar imaging equipment.
Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning
Firstly, we introduce different extraction strategies to split the text into two sets of logical units, and construct the logical graph and the syntax graph respectively.
Ranked #15 on
Reading Comprehension
on ReClor
Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding
More specifically, our dataset contains 50 hours of annotated videos to localize relevant animal behavior segments in long videos for the video grounding task, 30K video sequences for the fine-grained multi-label action recognition task, and 33K frames for the pose estimation task, which correspond to a diverse range of animals with 850 species across 6 major animal classes.
Image Segmentation with Adaptive Spatial Priors from Joint Registration
In medical imaging for instance, intensity inhomogeneity and noise are common.
Prompt-based Generative Approach towards Multi-Hierarchical Medical Dialogue State Tracking
The dialogue state tracking (DST) module in the medical dialogue system which interprets utterances into the machine-readable structure for downstream tasks is particularly challenging.
Cluster Head Detection for Hierarchical UAV Swarm With Graph Self-supervised Learning
We first propose a graph attention self-supervised learning algorithm (GASSL) to detect the HUAVs of a single UAV cluster, where the GASSL can fit the IFS at the same time.
Bridging the Source-to-target Gap for Cross-domain Person Re-Identification with Intermediate Domains
Instead of directly aligning the source and target domains against each other, we propose to align the source and target domains against their intermediate domains for a smooth knowledge transfer.
Towards Class-agnostic Tracking Using Feature Decorrelation in Point Clouds
In this work, we investigate a more challenging task in the LiDAR point clouds, class-agnostic tracking, where a general model is supposed to be learned for any specified targets of both observed and unseen categories.
Theoretical Analysis of Deep Neural Networks in Physical Layer Communication
In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques, and also drive their cost in terms of computational complexity.
On Almost Sure Convergence Rates of Stochastic Gradient Methods
We further provide last-iterate almost sure convergence rates analysis for stochastic gradient methods on weakly convex smooth functions, in contrast with most existing results in the literature that only provide convergence in expectation for a weighted average of the iterates.
Uncertainty Modeling for Out-of-Distribution Generalization
In this paper, we improve the network generalization ability by modeling the uncertainty of domain shifts with synthesized feature statistics during training.
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.
Meta Agent Teaming Active Learning for Pose Estimation
The existing pose estimation approaches often require a large number of annotated images to attain good estimation performance, which are laborious to acquire.
En-Compactness: Self-Distillation Embedding & Contrastive Generation for Generalized Zero-Shot Learning
Our ICCE promotes intra-class compactness with inter-class separability on both seen and unseen classes in the embedding space and visual feature space.
Under-Approximate Reachability Analysis for a Class of Linear Systems with Inputs
Under-approximations of reachable sets and tubes have been receiving growing research attention due to their important roles in control synthesis and verification.
3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve
In this paper, we propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE).
MoCA: Incorporating Multi-stage Domain Pretraining and Cross-guided Multimodal Attention for Textbook Question Answering
Textbook Question Answering (TQA) is a complex multimodal task to infer answers given large context descriptions and abundant diagrams.
Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides
Conclusion: Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC.
LVIS Challenge Track Technical Report 1st Place Solution: Distribution Balanced and Boundary Refinement for Large Vocabulary Instance Segmentation
Our method focuses on the problem in following two aspects: the long-tail distribution and the segmentation quality of mask and boundary.
Learning First-Order Rules with Relational Path Contrast for Inductive Relation Reasoning
Relation reasoning in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm is learning the embeddings of relations and entities, which is limited to a transductive setting and has restriction on processing unseen entities in an inductive situation.
Disarranged Zone Learning (DZL): An unsupervised and dynamic automatic stenosis recognition methodology based on coronary angiography
Hence, the prediction accuracy of DZL is used as an approximator of coronary stenosis indicator.
Recent Advances of Continual Learning in Computer Vision: An Overview
In contrast to batch learning where all training data is available at once, continual learning represents a family of methods that accumulate knowledge and learn continuously with data available in sequential order.
Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation
Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i. e., class centers) in a hyper-sphere manifold.
RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions
Then we extend SOLOV2 to capture temporal information in video to learn motion information, and propose a moving object instance segmentation network with RiWFPN called RiWNet.
Learning Inner-Group Relations on Point Clouds
We further verify the expandability of RPNet, in terms of both depth and width, on the tasks of classification and segmentation.
Ranked #9 on
Semantic Segmentation
on ScanNet
The Multi-Modal Video Reasoning and Analyzing Competition
In this paper, we introduce the Multi-Modal Video Reasoning and Analyzing Competition (MMVRAC) workshop in conjunction with ICCV 2021.
Seirios: Leveraging Multiple Channels for LoRaWAN Indoor and Outdoor Localization
By exploiting both the original and the conjugate of the physical layer, Seirios can resolve the direct path from multiple reflectors in both indoor and outdoor environments.
Dual-Tuning: Joint Prototype Transfer and Structure Regularization for Compatible Feature Learning
It is a heavy workload to re-extract features of the whole database every time. Feature compatibility enables the learned new visual features to be directly compared with the old features stored in the database.
Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation
In this work, we propose two simple yet effective texture randomization mechanisms, Global Texture Randomization (GTR) and Local Texture Randomization (LTR), for Domain Generalization based SRSS.
IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID
To ensure these two properties to better characterize appropriate intermediate domains, we enforce the bridge losses on intermediate domains' prediction space and feature space, and enforce a diversity loss on the two domain factors.
Domain Adaptive Person Re-Identification
Person Re-Identification
Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning
We investigate unsupervised representation learning for skeleton action recognition, and design a novel skeleton cloud colorization technique that is capable of learning skeleton representations from unlabeled skeleton sequence data.
DeceFL: A Principled Decentralized Federated Learning Framework
Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks, such as federated learning, most state-of-the-art frameworks are built still in a centralized way, in which a central client is needed for collecting and distributing model information (instead of data itself) from every other client, leading to high communication pressure and high vulnerability when there exists a failure at or attack on the central client.
A Low Complexity Learning-based Channel Estimation for OFDM Systems with Online Training
Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission.
Resilient UAV Swarm Communications with Graph Convolutional Neural Network
Numerical results show that the proposed algorithms can rebuild the communication connectivity of the USNET more quickly than the existing algorithms under both one-off UEDs and general UEDs.
Interventional Video Grounding with Dual Contrastive Learning
2) Meanwhile, we introduce a dual contrastive learning approach (DCL) to better align the text and video by maximizing the mutual information (MI) between query and video clips, and the MI between start/end frames of a target moment and the others within a video to learn more informative visual representations.
QFCNN: Quantum Fourier Convolutional Neural Network
The neural network and quantum computing are both significant and appealing fields, with their interactive disciplines promising for large-scale computing tasks that are untackled by conventional computers.
Person30K: A Dual-Meta Generalization Network for Person Re-Identification
Recently, person re-identification (ReID) has vastly benefited from the surging waves of data-driven methods.
Conterfactual Generative Zero-Shot Semantic Segmentation
In this work, we consider counterfactual methods to avoid the confounder in the original model.
Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition
In this paper, we propose a novel knowledge-aware deep framework that incorporates some clinical knowledge into collaborative learning of two important melanoma diagnosis tasks, i. e., skin lesion segmentation and melanoma recognition.
Opening the Black Box of Deep Neural Networks in Physical Layer Communication
Deep Neural Network (DNN)-based physical layer techniques are attracting considerable interest due to their potential to enhance communication systems.
Generalizable Person Re-identification with Relevance-aware Mixture of Experts
Specifically, we propose a decorrelation loss to make the source domain networks (experts) keep the diversity and discriminability of individual domains' characteristics.
Safety-Critical Control of Stochastic Systems using Stochastic Control Barrier Functions
We propose a notion of stochastic control barrier functions (SCBFs)and show that SCBFs can significantly reduce the control efforts, especially in the presence of noise, compared to stochastic reciprocal control barrier functions (SRCBFs), and offer a less conservative estimation of safety probability, compared to stochastic zeroing control barrier functions (SZCBFs).
A Specification-Guided Framework for Temporal Logic Control of Nonlinear Systems
We show that the proposed algorithm is sound for full LTL specifications, and robustly complete for specifications recognizable by deterministic B\"uchi automata (DBA), the latter in the sense that control strategies can be found whenever the given specification can be satisfied with additional bounded disturbances.
UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles
Human behavior understanding with unmanned aerial vehicles (UAVs) is of great significance for a wide range of applications, which simultaneously brings an urgent demand of large, challenging, and comprehensive benchmarks for the development and evaluation of UAV-based models.
Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models
We find that mix-review effectively regularizes the finetuning process, and the forgetting problem is alleviated to some extent.
Rank-One Prior: Toward Real-Time Scene Recovery
To improve visual quality under different weather/imaging conditions, we propose a real-time light correction method to recover the degraded scenes in the cases of sandstorms, underwater, and haze.
SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events
In this paper, we create a novel dataset, SUTD-TrafficQA (Traffic Question Answering), which takes the form of video QA based on the collected 10, 080 in-the-wild videos and annotated 62, 535 QA pairs, for benchmarking the cognitive capability of causal inference and event understanding models in complex traffic scenarios.
Ranked #1 on
Video Question Answering
on SUTD-TrafficQA
Constrained Radar Waveform Design for Range Profiling
Range profiling refers to the measurement of target response along the radar slant range.
RL-CSDia: Representation Learning of Computer Science Diagrams
Another type of diagrams such as from Computer Science is composed of graphics containing complex topologies and relations, and research on this type of diagrams is still blank.
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning
How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world.
Else-Net: Elastic Semantic Network for Continual Action Recognition From Skeleton Data
This makes online continual action recognition a challenging task.
A Unified 3D Human Motion Synthesis Model via Conditional Variational Auto-Encoder
Notably, by considering this problem as a conditional generation process, we estimate a parametric distribution of the missing regions based on the input conditions, from which to sample and synthesize the full motion series.
Motion Adaptive Pose Estimation From Compressed Videos
A novel model, called Motion Adaptive Pose Net is proposed to exploit the compressed streams to efficiently decode pose sequences from videos.
Interaction via Bi-Directional Graph of Semantic Region Affinity for Scene Parsing
In this work, we treat each respective region in an image as a whole, and capture the structure topology as well as the affinity among different regions.
Dual-Refinement: Joint Label and Feature Refinement for Unsupervised Domain Adaptive Person Re-Identification
To this end, we propose a novel approach, called Dual-Refinement, that jointly refines pseudo labels at the off-line clustering phase and features at the on-line training phase, to alternatively boost the label purity and feature discriminability in the target domain for more reliable re-ID.
Domain Adaptive Person Re-Identification
Person Re-Identification
Human Action Recognition from Various Data Modalities: A Review
Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action.
Object Detection based on OcSaFPN in Aerial Images with Noise
On the basis of this, a novel octave convolution-based semantic attention feature pyramid network (OcSaFPN) is proposed to get higher accuracy in object detection with noise.
A ROM-accelerated parallel-in-time preconditioner for solving all-at-once systems from evolutionary PDEs
In this paper we propose to use model reduction techniques for speeding up the diagonalization-based parallel-in-time (ParaDIAG) preconditioner, for iteratively solving all-at-once systems from evolutionary PDEs.
Numerical Analysis Numerical Analysis Dynamical Systems
A Boundary Regression Model for Nested Named Entity Recognition
Then, a regression operation is introduced to regress boundaries of NEs in a sentence.
XTQA: Span-Level Explanations of the Textbook Question Answering
Textbook Question Answering (TQA) is a task that one should answer a diagram/non-diagram question given a large multi-modal context consisting of abundant essays and diagrams.
Skeleton-based Relational Reasoning for Group Activity Analysis
In this paper, we leverage the skeleton information to learn the interactions between the individuals straight from it.
Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images
Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models.
Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR
In this work, to measure the accuracy and efficiency for a latency-controlled streaming automatic speech recognition (ASR) application, we perform comprehensive evaluations on three popular training criteria: LF-MMI, CTC and RNN-T.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications
Despite the rapid progress in this topic, there are lacking of a comprehensive review, which is needed to summarize the current progress and provide the future directions.
Exact Phase Transitions of Model RB with Slower-Growing Domains
The second moment method has always been an effective tool to lower bound the satisfiability threshold of many random constraint satisfaction problems.
Robust Face Alignment by Multi-order High-precision Hourglass Network
Heatmap regression (HR) has become one of the mainstream approaches for face alignment and has obtained promising results under constrained environments.
Ranked #5 on
Face Alignment
on AFLW-19
Novel Parameter Estimation and Radar Detection Approaches for Multiple Point-like Targets: Designs and Comparisons
In this work, we develop and compare two innovative strategies for parameter estimation and radar detection of multiple point-like targets.
Smooth Converse Lyapunov-Barrier Theorems for Asymptotic Stability with Safety Constraints and Reach-Avoid-Stay Specifications
While the converse Lyapunov-barrier theorems are not constructive, as with classical converse Lyapunov theorems, we believe that the unified necessary and sufficient conditions with a single Lyapunov-barrier function are of theoretical interest and can hopefully shed some light on computational approaches.
Radar Adaptive Detection Architectures for Heterogeneous Environments
In this paper, four adaptive radar architectures for target detection in heterogeneous Gaussian environments are devised.
Improving probability selecting based weights for Satisfiability Problem
SelectNTS is an improved probability selecting based local search algorithm for SAT problem.
On the Convergence of Reinforcement Learning with Monte Carlo Exploring Starts
A basic simulation-based reinforcement learning algorithm is the Monte Carlo Exploring States (MCES) method, also known as optimistic policy iteration, in which the value function is approximated by simulated returns and a greedy policy is selected at each iteration.
Fine Timing and Frequency Synchronization for MIMO-OFDM: An Extreme Learning Approach
Multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) is a key technology component in the evolution towards cognitive radio (CR) in next-generation communication in which the accuracy of timing and frequency synchronization significantly impacts the overall system performance.
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet
The automatic and objective medical diagnostic model can be valuable to achieve early cancer detection, and thus reducing the mortality rate.
Ranked #1 on
Tumor Segmentation
on DigestPath
MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation
To the best of our knowledge, this work is the first extremely lightweight neural network trained on monocular video sequences for real-time unsupervised monocular depth estimation, which opens up the possibility of implementing deep learning-based real-time unsupervised monocular depth prediction on low-cost embedded devices.
Can Synthetic Data Improve Object Detection Results for Remote Sensing Images?
Deep learning approaches require enough training samples to perform well, but it is a challenge to collect enough real training data and label them manually.
KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis
Knowledge graphs (KGs) have become the preferred technology for representing, sharing and adding knowledge to modern AI applications.
DeepSSM: Deep State-Space Model for 3D Human Motion Prediction
In contrast to prior works, we improve the multi-order modeling ability of human motion systems for more accurate predictions by building a deep state-space model (DeepSSM).
ParaDiag: parallel-in-time algorithms based on the diagonalization technique
These results are obtained on the Tianhe-1 supercomputer in China and the SIUE Campus Cluster in the US and and we compare these results to the performance of parareal and MGRiT, two widely used PinT algorithms.
Numerical Analysis Numerical Analysis
Convex Shape Prior for Deep Neural Convolution Network based Eye Fundus Images Segmentation
This method is flexible and it can handle multiple objects and allow some of the objects to be convex.
Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images
Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice).
Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia
To this end, we develop a dual-sampling attention network to automatically diagnose COVID- 19 from the community acquired pneumonia (CAP) in chest computed tomography (CT).
Continuous Motion Planning with Temporal Logic Specifications using Deep Neural Networks
A na\"ive way of solving a motion planning problem with LTL specifications using reinforcement learning is to sample a trajectory and then assign a high reward for training if the trajectory satisfies the entire LTL formula.
Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images
Purpose: Using machine learning method to realize automatic severity assessment (non-severe or severe) of COVID-19 based on chest CT images, and to explore the severity-related features from the resulting assessment model.
Towards Cognitive Routing based on Deep Reinforcement Learning
Routing is one of the key functions for stable operation of network infrastructure.
Energy-based Periodicity Mining with Deep Features for Action Repetition Counting in Unconstrained Videos
Our work features two-fold: 1) An important insight that deep features extracted for action recognition can well model the self-similarity periodicity of the repetitive action is presented.
ZSTAD: Zero-Shot Temporal Activity Detection
An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos.
Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation
Accurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma.
Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning
Experimental results on glomeruli segmentation from renal biopsy images indicate that our network is able to improve segmentation performance on target type of stained images and use unlabeled data to achieve similar accuracy to labeled data.
Deep Convolutional Neural Networks with Spatial Regularization, Volume and Star-shape Priori for Image Segmentation
The novelty of our method is to interpret the softmax activation function as a dual variable in a variational problem, and thus many spatial priors can be imposed in the dual space.
Knowledge forest: a novel model to organize knowledge fragments
Facet trees can organize knowledge fragments with facet hyponymy to alleviate information overload.
D-SPIDER-SFO: A Decentralized Optimization Algorithm with Faster Convergence Rate for Nonconvex Problems
To the best of our knowledge, D-SPIDER-SFO achieves the state-of-the-art performance for solving nonconvex optimization problems on decentralized networks in terms of the computational cost.
Joint Embedding Learning of Educational Knowledge Graphs
However, in educational knowledge graphs, structural relationships are not the focus.
Extended Answer and Uncertainty Aware Neural Question Generation
In this paper, we study automatic question generation, the task of creating questions from corresponding text passages where some certain spans of the text can serve as the answers.
Performance Analysis on Machine Learning-Based Channel Estimation
In this situation, our analysis results can be applied to assess the performance and support the design of machine learning-based channel estimation.
Training ASR models by Generation of Contextual Information
Supervised ASR models have reached unprecedented levels of accuracy, thanks in part to ever-increasing amounts of labelled training data.
Efficient Dynamic WFST Decoding for Personalized Language Models
A second layer is a private cache that caches the graph that represents the personalized language model, which is only shared by the utterances from a particular user.
Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models
We find that mix-review effectively regularizes the finetuning process, and the forgetting problem is alleviated to some extent.
TrajectoryNet: a new spatio-temporal feature learning network for human motion prediction
And the global temporal co-occurrence features represent the co-occurrence relationship that different subsequences in a complex motion sequence are appeared simultaneously, which can be obtained automatically with our proposed TrajectoryNet by reorganizing the temporal information as the depth dimension of the input tensor.
Interaction Relational Network for Mutual Action Recognition
Our solution is able to achieve state-of-the-art performance on the traditional interaction recognition datasets SBU and UT, and also on the mutual actions from the large-scale dataset NTU RGB+D.
Ranked #1 on
Human Interaction Recognition
on SBU
PowerSGD: Powered Stochastic Gradient Descent Methods for Accelerated Non-Convex Optimization
In this paper, we propose a novel technique for improving the stochastic gradient descent (SGD) method to train deep networks, which we term \emph{PowerSGD}.
Nonlocal Patches based Gaussian Mixture Model for Image Inpainting
By a maximum a posteriori (MAP) estimation, we formulate a new regularization term according to the log-likelihood function of the mixture model.
Volume Preserving Image Segmentation with Entropic Regularization Optimal Transport and Its Applications in Deep Learning
Image segmentation with a volume constraint is an important prior for many real applications.
Structured Query Construction via Knowledge Graph Embedding
At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query.
A Regularized Convolutional Neural Network for Semantic Image Segmentation
That is, spatial regularity of the segmented objects is still a problem for CNNs.
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition.
Ranked #5 on
One-Shot 3D Action Recognition
on NTU RGB+D 120
HLO: Half-kernel Laplacian Operator for Surface Smoothing
This paper presents a simple yet effective method for feature-preserving surface smoothing.
Computational Geometry Graphics
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
To address this problem, we train the bidirectional model using the Cloze task, predicting the masked items in the sequence by jointly conditioning on their left and right context.
Highly Efficient Follicular Segmentation in Thyroid Cytopathological Whole Slide Image
In this paper, we propose a novel method for highly efficient follicular segmentation of thyroid cytopathological WSIs.
Skeleton-Based Online Action Prediction Using Scale Selection Network
Since there are significant temporal scale variations in the observed part of the ongoing action at different time steps, a novel window scale selection method is proposed to make our network focus on the performed part of the ongoing action and try to suppress the possible incoming interference from the previous actions at each step.
Ranked #42 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Feature Boosting Network For 3D Pose Estimation
Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation.
Deep Neural Network Aided Scenario Identification in Wireless Multi-path Fading Channels
This letter illustrates our preliminary works in deep nerual network (DNN) for wireless communication scenario identification in wireless multi-path fading channels.
Sentence Suggestion of Japanese Functional Expressions for Chinese-speaking Learners
We present a computer-assisted learning system, Jastudy, which is particularly designed for Chinese-speaking learners of Japanese as a second language (JSL) to learn Japanese functional expressions with suggestion of appropriate example sentences.
A Variational Image Segmentation Model based on Normalized Cut with Adaptive Similarity and Spatial Regularization
Thus, the segmentation results of the existing Ncut method are largely dependent on a pre-constructed similarity measure on the graph since this measure is usually given empirically by users.
SSNet: Scale Selection Network for Online 3D Action Prediction
As there are significant temporal scale variations of the observed part of the ongoing action at different progress levels, we propose a novel window scale selection scheme to make our network focus on the performed part of the ongoing action and try to suppress the noise from the previous actions at each time step.
Deep Convolutional Neural Networks for Map-Type Classification
To facilitate establishing an automatic approach for accessing the needed map, this paper reports our investigation into using deep learning techniques to recognize seven types of map, including topographic map, terrain map, physical map, urban scene map, the National Map, 3D map, nighttime map, orthophoto map, and land cover classification map.
Convexity Shape Prior for Level Set based Image Segmentation Method
We apply our method to region and edge based level set segmentation models including Chan-Vese (CV) model with guarantee that the segmented region will be convex.
Variational based Mixed Noise Removal with CNN Deep Learning Regularization
In this paper, the traditional model based variational method and learning based algorithms are naturally integrated to address mixed noise removal problem.
Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection
In this paper, we propose a new Multi-Glimpse LSTM (MG-LSTM) network, in which multi-scale contextual information is sequentially integrated to promote the human detection performance.
Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks
In this paper, we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action recognition.
Ranked #40 on
Skeleton Based Action Recognition
on NTU RGB+D 120
PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking
Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge.
Deep Level Sets for Salient Object Detection
Our method drives the network to learn a Level Set function for salient objects so it can output more accurate boundaries and compact saliency.
Global Context-Aware Attention LSTM Networks for 3D Action Recognition
Hence we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for 3D action recognition, which is able to selectively focus on the informative joints in the action sequence with the assistance of global contextual information.
Ranked #7 on
One-Shot 3D Action Recognition
on NTU RGB+D 120
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates
Skeleton-based human action recognition has attracted a lot of research attention during the past few years.
Ranked #6 on
One-Shot 3D Action Recognition
on NTU RGB+D 120
A Procedural Texture Generation Framework Based on Semantic Descriptions
Finally, given a set of semantic descriptions, the diverse properties of the samples in the semantic space can lead the framework to find an appropriate generation model that uses appropriate parameters to produce a desired texture.
Perception Driven Texture Generation
In this paper, we propose a joint deep network model that combines adversarial training and perceptual feature regression for texture generation, while only random noise and user-defined perceptual attributes are required as input.
Simplification of Example Sentences for Learners of Japanese Functional Expressions
Learning functional expressions is one of the difficulties for language learners, since functional expressions tend to have multiple meanings and complicated usages in various situations.
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
To handle the noise and occlusion in 3D skeleton data, we introduce new gating mechanism within LSTM to learn the reliability of the sequential input data and accordingly adjust its effect on updating the long-term context information stored in the memory cell.
Ranked #8 on
Skeleton Based Action Recognition
on SBU
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Recent approaches in depth-based human activity analysis achieved outstanding performance and proved the effectiveness of 3D representation for classification of action classes.