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Found 400 papers, 176 papers with code
Semi-Dynamic Load Balancing: Efficient Distributed Learning in Non-Dedicated Environments
Efficient model training requires eliminating such stragglers, yet for modern ML workloads, existing load balancing strategies are inefficient and even infeasible.
Key Person Aided Re-identification in Partially Ordered Pedestrian Set
Ideally person re-identification seeks for perfect feature representation and metric model that re-identify all various pedestrians well in non-overlapping views at different locations with different camera configurations, which is very challenging.
One-Two-One Networks for Compression Artifacts Reduction in Remote Sensing
Compression artifacts reduction (CAR) is a challenging problem in the field of remote sensing.
Gabor Convolutional Networks
Steerable properties dominate the design of traditional filters, e. g., Gabor filters, and endow features the capability of dealing with spatial transformations.
Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions
Then, motion and shape cues are jointly used to generate robust and distinctive spatial-temporal interest points (STIPs): motion-based STIPs and shape-based STIPs.
An End-to-end 3D Convolutional Neural Network for Action Detection and Segmentation in Videos
A video is first divided into equal length clips and next for each clip a set of tube proposals are generated based on 3D CNN features.
Latent Constrained Correlation Filter
In this paper, we introduce an intermediate step -- solution sampling -- after the data sampling step to form a subspace, in which an optimal solution can be estimated.
Block building programming for symbolic regression
Symbolic regression that aims to detect underlying data-driven models has become increasingly important for industrial data analysis.
Multi-modal Aggregation for Video Classification
In this paper, we present a solution to Large-Scale Video Classification Challenge (LSVC2017) [1] that ranked the 1st place.
Manifold Constrained Low-Rank Decomposition
Low-rank decomposition (LRD) is a state-of-the-art method for visual data reconstruction and modelling.
Fast Modeling Methods for Complex System with Separable Features
In this paper, we analyze different types of separability of some real-world engineering equations and establish a mathematical model of generalized separable system (GS system).
A divide and conquer method for symbolic regression
This feature motivated us to develop a new method, divide and conquer (D&C), for symbolic regression, in which the target function is divided into a number of sub-functions and the sub-functions are then determined by any of a GP algorithm.
Latent Constrained Correlation Filters for Object Localization
There is a neglected fact in the traditional machine learning methods that the data sampling can actually lead to the solution sampling.
Elite Bases Regression: A Real-time Algorithm for Symbolic Regression
In this paper, a new non-evolutionary real-time algorithm for symbolic regression, Elite Bases Regression (EBR), is proposed.
Deep Spatio-temporal Manifold Network for Action Recognition
Visual data such as videos are often sampled from complex manifold.
Measuring and Predicting Tag Importance for Image Retrieval
Textual data such as tags, sentence descriptions are combined with visual cues to reduce the semantic gap for image retrieval applications in today's Multimodal Image Retrieval (MIR) systems.
Output Constraint Transfer for Kernelized Correlation Filter in Tracking
Kernelized Correlation Filter (KCF) is one of the state-of-the-art object trackers.
GAL: A Global-Attributes Assisted Labeling System for Outdoor Scenes
The proposed Global-attributes Assisted Labeling (GAL) system exploits both local features and global attributes.
Fast Iteratively Reweighted Least Squares Algorithms for Analysis-Based Sparsity Reconstruction
The convergence rate of the proposed algorithm is almost the same as that of the traditional IRLS algorithms, that is, exponentially fast.
SIRF: Simultaneous Image Registration and Fusion in A Unified Framework
In this paper, we propose a novel method for image fusion with a high-resolution panchromatic image and a low-resolution multispectral image at the same geographical location.
Forest Sparsity for Multi-channel Compressive Sensing
In this paper, we investigate a new compressive sensing model for multi-channel sparse data where each channel can be represented as a hierarchical tree and different channels are highly correlated.
Rain Removal By Image Quasi-Sparsity Priors
We present a novel rain removal method in this paper, which consists of two steps, i. e., detection of rain streaks and reconstruction of the rain-removed image.
Compressive Sensing MRI with Wavelet Tree Sparsity
On the other side, some algorithms have been proposed for tree sparsity regularization, but few of them has validated the benefit of tree structure in CS-MRI.
Dense Semantic and Topological Correspondence of 3D Faces without Landmarks
The dense correspondence goal is revisited in two perspectives: semantic and topological correspondence.
Preconditioning for Accelerated Iteratively Reweighted Least Squares in Structured Sparsity Reconstruction
In this paper, we propose a novel algorithm for structured sparsity reconstruction.
Image Fusion with Local Spectral Consistency and Dynamic Gradient Sparsity
In this paper, we propose a novel method for image fusion from a high resolution panchromatic image and a low resolution multispectral image at the same geographical location.
Deep Sparse Representation for Robust Image Registration
The definition of the similarity measure is an essential component in image registration.
Binary Coding for Partial Action Analysis With Limited Observation Ratios
Extensive experiments on four realistic action datasets in terms of three tasks (i. e., partial action retrieval, recognition and prediction) clearly show the superiority of PRBC over the state-of-the-art methods, along with significantly reduced memory load and computational costs during the online test.
SinoCoreferencer: An End-to-End Chinese Event Coreference Resolver
Compared to entity coreference resolution, there is a relatively small amount of work on event coreference resolution.
GeoCapsNet: Aerial to Ground view Image Geo-localization using Capsule Network
The task of cross-view image geo-localization aims to determine the geo-location (GPS coordinates) of a query ground-view image by matching it with the GPS-tagged aerial (satellite) images in a reference dataset.
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks.
An Efficient 3D CNN for Action/Object Segmentation in Video
Convolutional Neural Network (CNN) based image segmentation has made great progress in recent years.
Ranked #64 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images
Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart.
Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation
In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steady-state free precession (bSSFP) images, which are easier to acquire.
Enhanced skeleton visualization for view invariant human action recognition
First, a sequence-based view invariant transform is developed to eliminate the effect of view variations on spatio-temporal locations of skeleton joints.
Ranked #2 on
Skeleton Based Action Recognition
on UWA3D
Renyi Differentially Private ADMM for Non-Smooth Regularized Optimization
In this paper we consider the problem of minimizing composite objective functions consisting of a convex differentiable loss function plus a non-smooth regularization term, such as $L_1$ norm or nuclear norm, under R\'enyi differential privacy (RDP).
MGHRL: Meta Goal-generation for Hierarchical Reinforcement Learning
Most meta reinforcement learning (meta-RL) methods learn to adapt to new tasks by directly optimizing the parameters of policies over primitive action space.
Essentia: Mining Domain-Specific Paraphrases with Word-Alignment Graphs
Paraphrases are important linguistic resources for a wide variety of NLP applications.
Multilingual Dialogue Generation with Shared-Private Memory
Existing dialog systems are all monolingual, where features shared among different languages are rarely explored.
Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years.
HMTNet:3D Hand Pose Estimation from Single Depth Image Based on Hand Morphological Topology
In this paper, we propose a method that takes advantage of human hand morphological topology (HMT) structure to improve the pose estimation performance.
Robotics Human-Computer Interaction
RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices
Therefore, high efficiency object detectors on CPU-only devices are urgently-needed in industry.
Approximating Trajectory Constraints with Machine Learning -- Microgrid Islanding with Frequency Constraints
In this paper, we introduce a deep learning aided constraint encoding method to tackle the frequency-constraint microgrid scheduling problem.
Video Anomaly Detection for Smart Surveillance
Temporal localization (i. e. indicating the start and end frames of the anomaly event in a video) is referred to as frame-level detection.
Enhancing Review Comprehension with Domain-Specific Commonsense
In this paper, we introduce xSense, an effective system for review comprehension using domain-specific commonsense knowledge bases (xSense KBs).
Action recognition in real-world videos
Temporal localization (i. e. indicating the start and end frames of the action in a video) is referred to as frame-level detection.
PCA-SRGAN: Incremental Orthogonal Projection Discrimination for Face Super-resolution
Generative Adversarial Networks (GAN) have been employed for face super resolution but they bring distorted facial details easily and still have weakness on recovering realistic texture.
CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks
To this end, a Child-Parent (CP) model is introduced to a differentiable NAS to search the binarized architecture (Child) under the supervision of a full-precision model (Parent).
Recognizing Exercises and Counting Repetitions in Real Time
Artificial intelligence technology has made its way absolutely necessary in a variety of industries including the fitness industry.
Public discourse and sentiment during the COVID-19 pandemic: using Latent Dirichlet Allocation for topic modeling on Twitter
The study aims to understand Twitter users' discourse and psychological reactions to COVID-19.
Revisiting Street-to-Aerial View Image Geo-localization and Orientation Estimation
Street-to-aerial image geo-localization, which matches a query street-view image to the GPS-tagged aerial images in a reference set, has attracted increasing attention recently.
Twitter discussions and emotions about COVID-19 pandemic: a machine learning approach
Across all identified topics, the dominant sentiments for the spread of coronavirus are anticipation that measures that can be taken, followed by a mixed feeling of trust, anger, and fear for different topics.
Robust Federated Recommendation System
We argue that the key to Byzantine detection is monitoring of gradients of the model parameters of clients.
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR.
Deep Generative Model-based Quality Control for Cardiac MRI Segmentation
Our approach provides a real-time and model-agnostic quality control for cardiac MRI segmentation, which has the potential to be integrated into clinical image analysis workflows.
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction
Our key contribution is that the VAE disentangles the latent space based on `explanations' drawn from existing clinical knowledge.
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
This is achieved by layerwise imitation, that is, forcing the thin network to mimic the intermediate outputs of the wide network from layer to layer.
Stochastic Adaptive Line Search for Differentially Private Optimization
In this paper, we introduce a stochastic variant of classic backtracking line search algorithm that satisfies R\'enyi differential privacy.
Adversarial Self-Supervised Data-Free Distillation for Text Classification
To the best of our knowledge, our framework is the first data-free distillation framework designed for NLP tasks.
On the Transformer Growth for Progressive BERT Training
Due to the excessive cost of large-scale language model pre-training, considerable efforts have been made to train BERT progressively -- start from an inferior but low-cost model and gradually grow the model to increase the computational complexity.
Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery
Fast and effective responses are required when a natural disaster (e. g., earthquake, hurricane, etc.)
Ranked #3 on
2D Semantic Segmentation
on xBD
Nonvolatile electric control of exciton complex in monolayer MoSe$_2$ with two dimensional ferroelectric CuInP$_2$S$_6$
With the saturated ferroelectric polarization of CIPS, electron-doped or hole-doped MoSe$_2$ is realized in a single device with a large carrier density tunability up to $5\times 10^{12}$cm$^{-2}$.
Materials Science
Robust Synthesis of Wind Turbine Generators to Support Microgrid Frequency Considering Linearization-Induced Uncertainty
The capability to switch between grid-connected and islanded modes has promoted adoption of microgrid technology for powering remote locations.
A3D: Adaptive 3D Networks for Video Action Recognition
Even under the same computational constraints, the performance of our adaptive networks can be significantly boosted over the baseline counterparts by the mutual training along three dimensions.
NOMA for Energy-Efficient LiFi-Enabled Bidirectional IoT Communication
In order to improve the energy efficiency (EE) of the bidirectional LiFi-IoT system, non-orthogonal multiple access (NOMA) with a quality-of-service (QoS)-guaranteed optimal power allocation (OPA) strategy is applied to maximize the EE of the system.
Privacy and Robustness in Federated Learning: Attacks and Defenses
Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries.
Towards a category-extended object detector with limited data
Object detectors are typically learned on fully-annotated training data with fixed predefined categories.
Structural and dynamic disorder, not ionic trapping, controls charge transport in highly doped conducting polymers
Doped organic semiconductors are critical to emerging device applications, including thermoelectrics, bioelectronics, and neuromorphic computing devices.
Materials Science
A Transferable Anti-Forensic Attack on Forensic CNNs Using A Generative Adversarial Network
In this paper, we propose a new anti-forensic attack framework designed to remove forensic traces left by a variety of manipulation operations.
Bilateral attention decoder: A lightweight decoder for real-time semantic segmentation
To verify the efficiency of the proposed bilateral attention decoder, we adopt a lightweight network as the backbone and compare our proposed method with other state-of-the-art real-time semantic segmentation methods on the Cityscapes and Camvid datasets.
Guided Interpolation for Adversarial Training
To enhance adversarial robustness, adversarial training learns deep neural networks on the adversarial variants generated by their natural data.
A Dataset and Benchmark for Malaria Life-Cycle Classification in Thin Blood Smear Images
Malaria microscopy, microscopic examination of stained blood slides to detect parasite Plasmodium, is considered to be a gold-standard for detecting life-threatening disease malaria.
Addressing Action Oscillations through Learning Policy Inertia
We propose Nested Policy Iteration as a general training algorithm for PIC-augmented policy which ensures monotonically non-decreasing updates under some mild conditions.
Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy
Randomized smoothing (RS) is an effective and scalable technique for constructing neural network classifiers that are certifiably robust to adversarial perturbations.
Optimal Scheduling of Integrated Demand Response-Enabled Integrated Energy Systems with Uncertain Renewable Generations: A Stackelberg Game Approach
In order to balance the interests of integrated energy operator (IEO) and users, a novel Stackelberg game-based optimization framework is proposed for the optimal scheduling of integrated demand response (IDR)-enabled integrated energy systems with uncertain renewable generations, where the IEO acts as the leader who pursues the maximization of his profits by setting energy prices, while the users are the follower who adjusts energy consumption plans to minimize their energy costs.
Killing One Bird with Two Stones: Model Extraction and Attribute Inference Attacks against BERT-based APIs
In this work, we bridge this gap by first presenting an effective model extraction attack, where the adversary can practically steal a BERT-based API (the target/victim model) by only querying a limited number of queries.
SGPT: Semantic Graphs based Pre-training for Aspect-based Sentiment Analysis
Previous studies show effective of pre-trained language models for sentiment analysis.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Fairness-Aware Unsupervised Feature Selection
Feature selection is a prevalent data preprocessing paradigm for various learning tasks.
Energy Aligning for Biased Models
Training on class-imbalanced data usually results in biased models that tend to predict samples into the majority classes, which is a common and notorious problem.
SynET: Synonym Expansion using Transitivity
Extensive experiments on a real-world dataset show the effectiveness of our approach.
Deep Learning-Aided OFDM-Based Generalized Optical Quadrature Spatial Modulation
In this paper, we propose an orthogonal frequency division multiplexing (OFDM)-based generalized optical quadrature spatial modulation (GOQSM) technique for multiple-input multiple-output optical wireless communication (MIMO-OWC) systems.
Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation
In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures.
A Novel Attribute Reconstruction Attack in Federated Learning
We perform the first systematic evaluation of attribute reconstruction attack (ARA) launched by the malicious server in the FL system, and empirically demonstrate that the shared epoch-averaged local model gradients can reveal sensitive attributes of local training data of any victim participant.
CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation
Meta learning, especially gradient based one, can be adopted to tackle this problem by learning initial parameters of the model and thus allowing fast adaptation to a specific task from limited data examples.
Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction
Evaluation of predictive deep learning (DL) models beyond conventional performance metrics has become increasingly important for applications in sensitive environments like healthcare.
DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Blind Detection
Generalized optical multiple-input multiple-output (GOMIMO) techniques have been recently shown to be promising for high-speed optical wireless communication (OWC) systems.
Learning Pseudometric-based Action Representations for Offline Reinforcement Learning
Offline reinforcement learning is a promising approach for practical applications since it does not require interactions with real-world environments.
Label-Occurrence-Balanced Mixup for Long-tailed Recognition
When applying mixup to long-tailed data, a label suppression issue arises, where the frequency of label occurrence for each class is imbalanced and most of the new examples will be completely or partially assigned with head labels.
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing
Federated Learning (FL) over wireless multi-hop edge computing networks, i. e., multi-hop FL, is a cost-effective distributed on-device deep learning paradigm.
CycleFlow: Purify Information Factors by Cycle Loss
SpeechFlow is a powerful factorization model based on information bottleneck (IB), and its effectiveness has been reported by several studies.
Self-learned Intelligence for Integrated Decision and Control of Automated Vehicles at Signalized Intersections
This paper develops the dynamic permutation state representation in the framework of integrated decision and control (IDC) to handle signalized intersections with mixed traffic flows.
Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward Machines
In this paper, we propose Lifelong reinforcement learning with Sequential linear temporal logic formulas and Reward Machines (LSRM), which enables an agent to leverage previously learned knowledge to fasten learning of logically specified tasks.
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge
To solve such MDP, multi-agent reinforcement learning (MA-RL) algorithms along with domain-specific action space refining schemes are developed, which online learn the delay-minimum forwarding paths to minimize the model exchange latency between the edge devices (i. e., workers) and the remote server.
Boost Distribution System Restoration with Emergency Communication Vehicles Considering Cyber-Physical Interdependence
Enhancing restoration capabilities of distribution systems is one of the main strategies for resilient power systems to cope with extreme events.
A closer look at network resolution for efficient network design
We propose a framework to mutually learn from different input resolutions and network widths.
What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator
The value function lies in the heart of Reinforcement Learning (RL), which defines the long-term evaluation of a policy in a given state.
A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems
Facing the difficulty of expensive and trivial data collection and annotation, how to make a deep learning-based short-term voltage stability assessment (STVSA) model work well on a small training dataset is a challenging and urgent problem.
Hierarchical Stochastic Scheduling of Multi-Community Integrated Energy Systems in Uncertain Environments via Stackelberg Game
An operating entity utilizing community-integrated energy systems with a large number of small-scale distributed energy sources can easily trade with existing distribution markets.
Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning
Despite of achieving great success in real-world applications, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, i. e., data efficiency, lack of the interpretability and transferability.
Singularity: Planet-Scale, Preemptive and Elastic Scheduling of AI Workloads
At the heart of Singularity is a novel, workload-aware scheduler that can transparently preempt and elastically scale deep learning workloads to drive high utilization without impacting their correctness or performance, across a global fleet of AI accelerators (e. g., GPUs, FPGAs).
Interactive Audio-text Representation for Automated Audio Captioning with Contrastive Learning
Automated Audio captioning (AAC) is a cross-modal task that generates natural language to describe the content of input audio.
Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data
Noise-robust speech recognition systems require large amounts of training data including noisy speech data and corresponding transcripts to achieve state-of-the-art performances in face of various practical environments.
Resilient Distribution System Restoration with Communication Recovery by Drone Small Cells
Distribution system (DS) restoration after natural disasters often faces the challenge of communication failures to feeder automation (FA) facilities, resulting in prolonged load pick-up process.
GALA: Toward Geometry-and-Lighting-Aware Object Search for Compositing
To move a step further, this paper proposes GALA (Geometry-and-Lighting-Aware), a generic foreground object search method with discriminative modeling on geometry and lighting compatibility for open-world image compositing.
SEAL: A Large-scale Video Dataset of Multi-grained Spatio-temporally Action Localization
SEAL consists of two kinds of annotations, SEAL Tubes and SEAL Clips.
Faster-TAD: Towards Temporal Action Detection with Proposal Generation and Classification in a Unified Network
In this paper, we propose a unified network for TAD, termed Faster-TAD, by re-purposing a Faster-RCNN like architecture.
Primal-dual Estimator Learning: an Offline Constrained Moving Horizon Estimation Method with Feasibility and Near-optimality Guarantees
Both the primal and dual estimators are learned from data using supervised learning techniques, and the explicit sample size is provided, which enables us to guarantee the quality of each learned estimator in terms of feasibility and optimality.
Self-critical Sequence Training for Automatic Speech Recognition
Although automatic speech recognition (ASR) task has gained remarkable success by sequence-to-sequence models, there are two main mismatches between its training and testing that might lead to performance degradation: 1) The typically used cross-entropy criterion aims to maximize log-likelihood of the training data, while the performance is evaluated by word error rate (WER), not log-likelihood; 2) The teacher-forcing method leads to the dependence on ground truth during training, which means that model has never been exposed to its own prediction before testing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Rethinking Reinforcement Learning based Logic Synthesis
Recently, reinforcement learning has been used to address logic synthesis by formulating the operator sequence optimization problem as a Markov decision process.
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation
For 3D medical image (e. g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly.
BANet: Motion Forecasting with Boundary Aware Network
We propose a motion forecasting model called BANet, which means Boundary-Aware Network, and it is a variant of LaneGCN.
Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders
Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community.
Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications
Federated Graph Machine Learning (FGML) is a promising solution to tackle this challenge by training graph machine learning models in a federated manner.
RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation
For the input-level, we design a new data augmentation technique as Phase MixUp, which highlights task-relevant objects in the interpolations, thus enhancing input-level regularization and class consistency for target models.
Binary Representation via Jointly Personalized Sparse Hashing
Different personalized subspaces are constructed to reflect category-specific attributes for different clusters, adaptively mapping instances within the same cluster to the same Hamming space.
SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-supervised Skeleton Action Recognition
Given the unmasked skeleton sequence, the encoder is fine-tuned for the action recognition task.
An Analysis of Deep Reinforcement Learning Agents for Text-based Games
Text-based games(TBG) are complex environments which allow users or computer agents to make textual interactions and achieve game goals. In TBG agent design and training process, balancing the efficiency and performance of the agent models is a major challenge.
Conquering the Communication Constraints to Enable Large Pre-Trained Models in Federated Learning
Recently, the use of small pre-trained models has been shown effective in federated learning optimization and improving convergence.
Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model
End-to-end autonomous driving provides a feasible way to automatically maximize overall driving system performance by directly mapping the raw pixels from a front-facing camera to control signals.
Rule Based Event Extraction for Artificial Social Intelligence
Natural language (as opposed to structured communication modes such as Morse code) is by far the most common mode of communication between humans, and can thus provide significant insight into both individual mental states and interpersonal dynamics.
The Extreme Cardiac MRI Analysis Challenge under Respiratory Motion (CMRxMotion)
The quality of cardiac magnetic resonance (CMR) imaging is susceptible to respiratory motion artifacts.
End-to-End Context-Aided Unicity Matching for Person Re-identification
Based on a natural assumption that images belonging to the same person identity should not match with images belonging to multiple different person identities across views, called the unicity of person matching on the identity level, we propose an end-to-end person unicity matching architecture for learning and refining the person matching relations.
Language-Assisted Deep Learning for Autistic Behaviors Recognition
In such cases, automatic recognition systems based on computer vision and machine learning (in particular deep learning) technology can alleviate this issue to a large extent.
State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning
The key idea of SA-PP is leveraging discounted stationary state distribution ratios between the learning policy and the offline dataset to modulate the degree of behavior regularization in a state-wise manner, so that pessimism can be implemented in a more appropriate way.
Leveraging Modality-specific Representations for Audio-visual Speech Recognition via Reinforcement Learning
Audio-visual speech recognition (AVSR) has gained remarkable success for ameliorating the noise-robustness of speech recognition.
When Do Curricula Work in Federated Learning?
Specifically, we aim to investigate how ordered learning principles can contribute to alleviating the heterogeneity effects in FL.
Finding Nontrivial Minimum Fixed Points in Discrete Dynamical Systems
Fixed points of such dynamical systems represent configurations to which the system converges.
Machine Learning-Based Secret Key Generation for IRS-assisted Multi-antenna Systems
Different from most previous works that adopt the iterative optimization to solve the problem, the proposed DNN based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN.
The Exploration of Knowledge-Preserving Prompts for Document Summarisation
Despite the great development of document summarisation techniques nowadays, factual inconsistencies between the generated summaries and the original texts still occur from time to time.
STAIR: Learning Sparse Text and Image Representation in Grounded Tokens
We extend the CLIP model and build a sparse text and image representation (STAIR), where the image and text are mapped to a sparse token space.
DELTA: degradation-free fully test-time adaptation
However, we find that two unfavorable defects are concealed in the prevalent adaptation methodologies like test-time batch normalization (BN) and self-learning.
Filtering Context Mitigates Scarcity and Selection Bias in Political Ideology Prediction
We propose a novel supervised learning approach for political ideology prediction (PIP) that is capable of predicting out-of-distribution inputs.
Delving into the Adversarial Robustness of Federated Learning
In this work, we propose a novel algorithm called Decision Boundary based Federated Adversarial Training (DBFAT), which consists of two components (local re-weighting and global regularization) to improve both accuracy and robustness of FL systems.
Delving into Identify-Emphasize Paradigm for Combating Unknown Bias
For challenge B, we point out that the gradient contribution statistics can be a reliable indicator to inspect whether the optimization is dominated by bias-aligned samples.
Unsupervised Noise adaptation using Data Simulation
Deep neural network based speech enhancement approaches aim to learn a noisy-to-clean transformation using a supervised learning paradigm.
Metric-oriented Speech Enhancement using Diffusion Probabilistic Model
Deep neural network based speech enhancement technique focuses on learning a noisy-to-clean transformation supervised by paired training data.
A Pathway Towards Responsible AI Generated Content
AI Generated Content (AIGC) has received tremendous attention within the past few years, with content generated in the format of image, text, audio, video, etc.
Highly Efficient Architecture of NewHope-NIST on FPGA using Low-Complexity NTT/INTT
First, both the pre-processing of NTT and the post-processing of INTT are merged into the fast Fourier transform (FFT) algorithm, which reduces N and 2N modular multiplications for N-point NTT and INTT, respectively.
DiffMesh: A Motion-aware Diffusion-like Framework for Human Mesh Recovery from Videos
While image-based HMR methods have achieved impressive results, they often struggle to recover humans in dynamic scenarios, leading to temporal inconsistencies and non-smooth 3D motion predictions due to the absence of human motion.
Ranked #56 on
3D Human Pose Estimation
on 3DPW
Towards Adversarially Robust Continual Learning
Recent studies show that models trained by continual learning can achieve the comparable performances as the standard supervised learning and the learning flexibility of continual learning models enables their wide applications in the real world.
Graph-Guided MLP-Mixer for Skeleton-Based Human Motion Prediction
In recent years, Graph Convolutional Networks (GCNs) have been widely used in human motion prediction, but their performance remains unsatisfactory.
Ranked #3 on
Human Pose Forecasting
on Human3.6M
$R^{2}$Former: Unified $R$etrieval and $R$eranking Transformer for Place Recognition
Visual Place Recognition (VPR) estimates the location of query images by matching them with images in a reference database.
TopNet: Transformer-based Object Placement Network for Image Compositing
Given a background image and a segmented object, the goal is to train a model to predict plausible placements (location and scale) of the object for compositing.
Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR
Second, during finetuning we propose a Transformer-based code predictor to accurately predict clean codes by modeling the global dependency of input noisy representations, which enables discovery and restoration of high-quality clean representations with reduced distortions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite
To foster advancements in this burgeoning field, we have launched an online open-source benchmark suite, referred to as UDTIRI.
Med-Tuning: Parameter-Efficient Transfer Learning with Fine-Grained Feature Enhancement for Medical Volumetric Segmentation
In this paper, we present the study on parameter-efficient transfer learning for medical volumetric segmentation and propose a new framework named Med-Tuning based on intra-stage feature enhancement and inter-stage feature interaction.
Secret Key Generation for IRS-Assisted Multi-Antenna Systems: A Machine Learning-Based Approach
Different from most previous works that adopt iterative optimisation to solve the problem, the proposed DNN-based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN.
Spatial-Temporal Networks for Antibiogram Pattern Prediction
In this paper, we propose a novel problem of antibiogram pattern prediction that aims to predict which patterns will appear in the future.
Dynamic Graph Learning With Content-Guided Spatial-Frequency Relation Reasoning for Deepfake Detection
To address this issue, we propose a Spatial-Frequency Dynamic Graph method to exploit the relation-aware features in spatial and frequency domains via dynamic graph learning.
Private Image Generation With Dual-Purpose Auxiliary Classifier
However, apart from this standard utility, we identify the "reversed utility" as another crucial aspect, which computes the accuracy on generated data of a classifier trained using real data, dubbed as real2gen accuracy (r2g%).
CM-MaskSD: Cross-Modality Masked Self-Distillation for Referring Image Segmentation
Referring image segmentation (RIS) is a fundamental vision-language task that intends to segment a desired object from an image based on a given natural language expression.
Text-based Person Search without Parallel Image-Text Data
Text-based person search (TBPS) aims to retrieve the images of the target person from a large image gallery based on a given natural language description.
DiffHand: End-to-End Hand Mesh Reconstruction via Diffusion Models
Hand mesh reconstruction from the monocular image is a challenging task due to its depth ambiguity and severe occlusion, there remains a non-unique mapping between the monocular image and hand mesh.
CN-Celeb-AV: A Multi-Genre Audio-Visual Dataset for Person Recognition
A comprehensive study was conducted to compare CN-Celeb-AV with two popular public AVPR benchmark datasets, and the results demonstrated that CN-Celeb-AV is more in line with real-world scenarios and can be regarded as a new benchmark dataset for AVPR research.
Alteration-free and Model-agnostic Origin Attribution of Generated Images
To overcome this problem, we first develop an alteration-free and model-agnostic origin attribution method via input reverse-engineering on image generation models, i. e., inverting the input of a particular model for a specific image.
Hierarchical Task Network Planning for Facilitating Cooperative Multi-Agent Reinforcement Learning
Exploring sparse reward multi-agent reinforcement learning (MARL) environments with traps in a collaborative manner is a complex task.
Unifying Large Language Models and Knowledge Graphs: A Roadmap
In this article, we present a forward-looking roadmap for the unification of LLMs and KGs.
When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions
The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual benefits, presents a unique opportunity to unlock new possibilities in AI research, and address critical challenges in AI and real-world applications.
Pay Attention to the Atlas: Atlas-Guided Test-Time Adaptation Method for Robust 3D Medical Image Segmentation
In addition, different from most existing TTA methods which restrict the adaptation to batch normalization blocks in the segmentation network only, we further exploit the use of channel and spatial attention blocks for improved adaptability at test time.
Separate-and-Aggregate: A Transformer-based Patch Refinement Model for Knowledge Graph Completion
Previous KGC methods typically represent knowledge graph entities and relations as trainable continuous embeddings and fuse the embeddings of the entity $h$ (or $t$) and relation $r$ into hidden representations of query $(h, r, ?
A Look into Causal Effects under Entangled Treatment in Graphs: Investigating the Impact of Contact on MRSA Infection
In this paper, we study the problem of causal effect estimation with treatment entangled in a graph.
Mystique: Deconstructing SVG Charts for Layout Reuse
To facilitate the reuse of existing charts, previous research has examined how to obtain a semantic understanding of a chart by deconstructing its visual representation into reusable components, such as encodings.
Learning Snippet-to-Motion Progression for Skeleton-based Human Motion Prediction
Existing Graph Convolutional Networks to achieve human motion prediction largely adopt a one-step scheme, which output the prediction straight from history input, failing to exploit human motion patterns.
A Safe DRL Method for Fast Solution of Real-Time Optimal Power Flow
Instead of integrating the constraint violation penalty with the reward function, its actor gradients are estimated by a Lagrange advantage function which is derived from two critic systems based on economic reward and violation cost.
GeoDTR+: Toward generic cross-view geolocalization via geometric disentanglement
We attribute this deficiency to the lack of ability to extract the geometric layout of visual features and models' overfitting to low-level details.
Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images
The best-performing model in terms of both classification accuracy and the most common calibration measure, expected calibration error (ECE) was the Confidence Weight method, a novel approach that weights the loss of samples to explicitly penalise confident incorrect predictions.
MoEController: Instruction-based Arbitrary Image Manipulation with Mixture-of-Expert Controllers
In this work, we propose a method with a mixture-of-expert (MOE) controllers to align the text-guided capacity of diffusion models with different kinds of human instructions, enabling our model to handle various open-domain image manipulation tasks with natural language instructions.
Towards Surveillance Video-and-Language Understanding: New Dataset, Baselines, and Challenges
Furthermore, we benchmark SOTA models for four multimodal tasks on this newly created dataset, which serve as new baselines for surveillance video-and-language understanding.
SEPT: Towards Efficient Scene Representation Learning for Motion Prediction
Motion prediction is crucial for autonomous vehicles to operate safely in complex traffic environments.
STAG: Enabling Low Latency and Low Staleness of GNN-based Services with Dynamic Graphs
With the collaborative serving mechanism, only part of node representations are updated during the update phase, and the final representations are calculated in the inference phase.
Beyond Sharing Weights in Decoupling Feature Learning Network for UAV RGB-Infrared Vehicle Re-Identification
Moreover, to meet cross-modality discrepancy and orientation discrepancy challenges, we present a hybrid weights decoupling network (HWDNet) to learn the shared discriminative orientation-invariant features.
Lifelong Sequence Generation with Dynamic Module Expansion and Adaptation
Inspired by the learning paradigm of humans, we propose Dynamic Module Expansion and Adaptation (DMEA), which enables the model to dynamically determine the architecture for acquiring new knowledge based on task correlation and select the most similar previous tasks to facilitate adaptation to new tasks.
Adaptive Quantization for Key Generation in Low-Power Wide-Area Networks
Physical layer key generation based on reciprocal and random wireless channels has been an attractive solution for securing resource-constrained low-power wide-area networks (LPWANs).
Generative error correction for code-switching speech recognition using large language models
In this work, we propose to leverage large language models (LLMs) and lists of hypotheses generated by an ASR to address the CS problem.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Knowledge Editing for Large Language Models: A Survey
Afterward, we provide an innovative taxonomy of KME techniques based on how the new knowledge is introduced into pre-trained LLMs, and investigate existing KME strategies while analyzing key insights, advantages, and limitations of methods from each category.
Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation
As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i. e. Med-DANet) has achieved a favorable accuracy and efficiency trade-off by dynamically selecting a suitable 2D candidate model from the pre-defined model bank for different slices.
MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval
Due to the success of large-scale visual-language pretraining (VLP) models and the widespread use of image-text retrieval in industry areas, it is now critically necessary to reduce the model size and streamline their mobile-device deployment.
An Empirical Study of Frame Selection for Text-to-Video Retrieval
In this paper, we make the first empirical study of frame selection for TVR.
Supported Trust Region Optimization for Offline Reinforcement Learning
Offline reinforcement learning suffers from the out-of-distribution issue and extrapolation error.
Decouple Content and Motion for Conditional Image-to-Video Generation
The goal of conditional image-to-video (cI2V) generation is to create a believable new video by beginning with the condition, i. e., one image and text. The previous cI2V generation methods conventionally perform in RGB pixel space, with limitations in modeling motion consistency and visual continuity.
LucidDreaming: Controllable Object-Centric 3D Generation
Nonetheless, achieving precise control over 3D generation continues to be an arduous task, as using text to control often leads to missing objects and imprecise locations.
FBChain: A Blockchain-based Federated Learning Model with Efficiency and Secure Communication
This article proposes Blockchain-based Federated Learning (FBChain) model for federated learning parameter communication to overcome the above two problems.
IL-NeRF: Incremental Learning for Neural Radiance Fields with Camera Pose Alignment
This poses a paradox as the necessary camera pose must be estimated from the entire dataset, even though the data arrives sequentially and future chunks are inaccessible.
Free-Editor: Zero-shot Text-driven 3D Scene Editing
To date, editing 3D scenes requires either re-training the model to adapt to various 3D edited scenes or design-specific methods for each special editing type.
NID-SLAM: Neural Implicit Representation-based RGB-D SLAM in dynamic environments
In this paper we present NID-SLAM, which significantly improves the performance of neural SLAM in dynamic environments.
Enhanced Few-Shot Class-Incremental Learning via Ensemble Models
To tackle overfitting challenge, we design a new ensemble model framework cooperated with data augmentation to boost generalization.
Dream360: Diverse and Immersive Outdoor Virtual Scene Creation via Transformer-Based 360 Image Outpainting
To this end, we propose a transformer-based 360 image outpainting framework called Dream360, which can generate diverse, high-fidelity, and high-resolution panoramas from user-selected viewports, considering the spherical properties of 360 images.
Learning Mutual Excitation for Hand-to-Hand and Human-to-Human Interaction Recognition
Recognizing interactive actions, including hand-to-hand interaction and human-to-human interaction, has attracted increasing attention for various applications in the field of video analysis and human-robot interaction.
