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Found 285 papers, 70 papers with code
Dual Scale-aware Adaptive Masked Knowledge Distillation for Object Detection
Different from previous methods in which global masking is performed on single-scale feature maps, we explore the scale-aware feature masking by performing feature distillation across various scales, such that the object-aware locality is encoded for improved feature reconstruction.
SFADNet: Spatio-temporal Fused Graph based on Attention Decoupling Network for Traffic Prediction
In recent years, traffic flow prediction has played a crucial role in the management of intelligent transportation systems.
Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images
Therefore, we fully explore these inter-spectral relationships and propose a Correlation and Continuity Network (CCNet) for HSI reconstruction from RGB images.
Completion as Enhancement: A Degradation-Aware Selective Image Guided Network for Depth Completion
In this paper, we introduce the Selective Image Guided Network (SigNet), a novel degradation-aware framework that transforms depth completion into depth enhancement for the first time.
Multi-View Fusion Neural Network for Traffic Demand Prediction
In this approach, spatial local features are extracted through the use of a graph convolutional network (GCN), and spatial global features are extracted using a cosine re-weighting linear attention mechanism (CLA).
Multimodal joint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional network
First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spatial global features.
Guided Real Image Dehazing using YCbCr Color Space
Image dehazing, particularly with learning-based methods, has gained significant attention due to its importance in real-world applications.
Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking
These temporal information tokens are used to guide the localization of the target in the next time state, establish long-range contextual relationships between video frames, and capture the temporal trajectory of the target.
Ranked #3 on
Rgb-T Tracking
on LasHeR
Pre-training a Density-Aware Pose Transformer for Robust LiDAR-based 3D Human Pose Estimation
In particular, compared with LPFormer on Waymo, we reduce the average MPJPE by $10. 0mm$.
Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving Video
In this paper, we study the challenging problem of simultaneously removing haze and estimating depth from real monocular hazy videos.
Neural Garment Dynamic Super-Resolution
In this paper, we introduce a lightweight, learning-based method for garment dynamic super-resolution, designed to efficiently enhance high-resolution, high-frequency details in low-resolution garment simulations.
IRS Aided Federated Learning: Multiple Access and Fundamental Tradeoff
Under the three protocols, we minimize the per-round latency subject to a given training loss by jointly optimizing the device scheduling, IRS phase-shifts, and communicationcomputation resource allocation.
Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach
In ultra-dense unmanned aerial vehicle (UAV) networks, it is challenging to coordinate the resource allocation and interference management among large-scale UAVs, for providing flexible and efficient service coverage to the ground users (GUs).
V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception
Previous studies have demonstrated the effectiveness of cooperative perception in extending the perception range and overcoming occlusions, thereby improving the safety of autonomous driving.
Affine Frequency Division Multiplexing with Index Modulation: Full Diversity Condition, Performance Analysis, and Low-Complexity Detection
Finally, BER simulation results confirm that our proposed CDD-AFDM-IM schemes with both the ML and DLMP detections outperform the benchmark schemes over the LTV channels.
Try-On-Adapter: A Simple and Flexible Try-On Paradigm
A key challenge of try-on is to generate realistic images of the model wearing the garments while preserving the details of the garments.
Movable Antenna Enhanced Networked Full-Duplex Integrated Sensing and Communication System
These challenges can be overcome by networked FD ISAC framework.
Kernel Orthogonality does not necessarily imply a Decrease in Feature Map Redundancy in CNNs: Convolutional Similarity Minimization
Nonetheless, it has been observed that CNNs suffer from redundancy in feature maps, leading to inefficient capacity utilization.
Deep Learning-Based Fatigue Cracks Detection in Bridge Girders using Feature Pyramid Networks
Then, models with the proposed structure of FPN for crack detection are developed.
Novel Object Synthesis via Adaptive Text-Image Harmony
In this paper, we study an object synthesis task that combines an object text with an object image to create a new object image.
Citywide Electric Vehicle Charging Demand Prediction Approach Considering Urban Region and Dynamic Influences
To tackle these issues, we propose a learning approach for citywide electric vehicle charging demand prediction, named CityEVCP.
DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain
In this paper, we introduce DCDepth, a novel framework for the long-standing monocular depth estimation task.
Organizing Unstructured Image Collections using Natural Language
Our framework, Text Driven Semantic Multiple Clustering (TeDeSC), uses text as a proxy to concurrently reason over large image collections, discover partitioning criteria, expressed in natural language, and reveal semantic substructures.
IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification
In light of this, this paper investigates a lightweight Interval Group Spatial-Spectral Mamba framework (IGroupSS-Mamba) for HSI classification, which allows for multi-directional and multi-scale global spatial-spectral information extraction in a grouping and hierarchical manner.
Computational Efficiency
Hyperspectral Image Classification
+2
An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains
The model is designed to be both an effective out-of-the-box solution, and a to be fine-tunable for downstream tasks, maximizing usability.
Towards TMA-Based Transmissive RIS Transceiver Enabled Downlink Communication Networks: A Consensus-ADMM Approach
This paper presents a novel multi-stream downlink communication system that utilizes a transmissive reconfigurable intelligent surface (RIS) transceiver.
3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation
We leverage 3D Gaussian representation to avoid the inefficiency caused by the extreme sparsity of coronary artery data and propose a Gaussian center predictor to overcome the noisy Gaussian initialization from ultra-sparse view projections.
FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks
In response, we propose FMBench, the first benchmark designed to evaluate the fairness of MLLMs performance across diverse demographic attributes.
Adversarial Federated Consensus Learning for Surface Defect Classification Under Data Heterogeneity in IIoT
In this paper, we propose a novel personalized FL (PFL) approach, named Adversarial Federated Consensus Learning (AFedCL), for the challenge of data heterogeneity across different clients in SDC.
Entropy Loss: An Interpretability Amplifier of 3D Object Detection Network for Intelligent Driving
Entropy Loss is formulated based on the functionality of feature compression networks within the perception model.
GMFL-Net: A Global Multi-geometric Feature Learning Network for Repetitive Action Counting
Then, to improve the feature representation from a global perspective, we also design a GBFL-Module that enhances the inter-dependencies between point-wise and channel-wise elements and combines them with the rich local information generated by the MIA-Module to synthesise a comprehensive and most representative global feature representation.
Passenger hazard perception based on EEG signals for highly automated driving vehicles
Enhancing the safety of autonomous vehicles is crucial, especially given recent accidents involving automated systems.
Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network
We utilize the Lyapunov method to decouple the formulated problem into a series of one-slot optimization problems and develop a two-stage optimization algorithm to achieve the optimal transmission power control and IoT device scheduling strategies.
Transmissive RIS Enabled Transceiver Systems:Architecture, Design Issues and Opportunities
Moreover, a near-far field channel model appropriate for this architecture is proposed.
Flatten: Video Action Recognition is an Image Classification task
In recent years, video action recognition, as a fundamental task in the field of video understanding, has been deeply explored by numerous researchers. Most traditional video action recognition methods typically involve converting videos into three-dimensional data that encapsulates both spatial and temporal information, subsequently leveraging prevalent image understanding models to model and analyze these data.
SSPA: Split-and-Synthesize Prompting with Gated Alignments for Multi-Label Image Recognition
With the help of flexible prompting and gated alignments, SSPA is generalizable to specific domains.
Garment Animation NeRF with Color Editing
Our approach infers garment dynamic features from body motion, providing a preliminary overview of garment structure.
DFMSD: Dual Feature Masking Stage-wise Knowledge Distillation for Object Detection
In this study, a novel dual feature-masking heterogeneous distillation framework termed DFMSD is proposed for object detection.
Unsupervised Domain Adaptive Lane Detection via Contextual Contrast and Aggregation
This paper focuses on two crucial issues in domain-adaptive lane detection, i. e., how to effectively learn discriminative features and transfer knowledge across domains.
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote Sensing
Specifically, a shared visual encoding method is developed to establish the spatial pattern interpretation relationships between the multi-scale representations of input images and various visual prompts.
ISPO: An Integrated Ontology of Symptom Phenotypes for Semantic Integration of Traditional Chinese Medical Data
Methods: To construct an integrated ontology of symptom phenotypes (ISPO), we manually annotated classical TCM textbooks and large-scale Chinese electronic medical records (EMRs) to collect symptom terms with support from a medical text annotation system.
Decision Transformer for IRS-Assisted Systems with Diffusion-Driven Generative Channels
In this paper, we propose a novel diffusion-decision transformer (D2T) architecture to optimize the beamforming strategies for intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) communication systems.
Deep Imbalanced Regression to Estimate Vascular Age from PPG Data: a Novel Digital Biomarker for Cardiovascular Health
Photoplethysmography (PPG) is emerging as a crucial tool for monitoring human hemodynamics, with recent studies highlighting its potential in assessing vascular aging through deep learning.
Ultrasound Report Generation with Cross-Modality Feature Alignment via Unsupervised Guidance
In this work, we propose a novel framework for automatic ultrasound report generation, leveraging a combination of unsupervised and supervised learning methods to aid the report generation process.
Trustworthy DNN Partition for Blockchain-enabled Digital Twin in Wireless IIoT Networks
To promote the efficiency and trustworthiness of DT for wireless IIoT networks, we propose a blockchain-enabled DT (B-DT) framework that employs deep neural network (DNN) partitioning technique and reputation-based consensus mechanism, wherein the DTs maintained at the gateway side execute DNN inference tasks using the data collected from their associated IIoT devices.
Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization
Driven by this issue, we propose a novel sparse FedAdam algorithm called FedAdam-SSM, wherein distributed devices sparsify the updates of local model parameters and moment estimates and subsequently upload the sparse representations to the centralized server.
MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space
In this paper, we introduce MambaLLIE, an implicit Retinex-aware low light enhancer featuring a global-then-local state space design.
3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification
To overcome the limitations of traditional Mamba, which is confined to modeling causal sequences and inadaptable to high-dimensional scenarios, a 3D-Spectral-Spatial Selective Scanning (3DSS) mechanism is introduced, which performs pixel-wise selective scanning on 3D hyperspectral tokens along the spectral and spatial dimensions.
Computational Efficiency
Hyperspectral Image Classification
+1
Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance
Firstly, we introduce a non-aligned reference frame matching module, leveraging an adaptive sliding window to match high-quality reference frames from clear videos.
Cooperative Visual-LiDAR Extrinsic Calibration Technology for Intersection Vehicle-Infrastructure: A review
Camera and LiDAR, the bedrock sensors in autonomous driving, exhibit expansive applicability.
Direct Learning of Mesh and Appearance via 3D Gaussian Splatting
The model creates an effective information pathway to supervise the learning of both 3DGS and mesh.
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
To evaluate the learning performance of T-SFL, we provide an upper bound on the global loss function.
Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint
In practical wireless systems, the communication links among nodes are usually unreliable due to wireless fading and receiver noise, consequently resulting in performance degradation of GNNs.
A Knowledge-driven Memetic Algorithm for the Energy-efficient Distributed Homogeneous Flow Shop Scheduling Problem
Furthermore, several algorithmic improvements including update strategy, local search strategy, and carbon reduction strategy are employed to improve the search performance of the algorithm.
Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Detection and Segmentation
The CoSAM utilizes sequence-based detection to guide the segmentation of metastatic lymph nodes in rectal cancer, contributing to improved localization performance for the segmentation model.
Integrated Sensing and Communication Under DISCO Physical-Layer Jamming Attacks
Moreover, a theoretical analysis is conducted to quantify the impact of DISCO jamming attacks.
Language Models Meet Anomaly Detection for Better Interpretability and Generalizability
This research explores the integration of language models and unsupervised anomaly detection in medical imaging, addressing two key questions: (1) Can language models enhance the interpretability of anomaly detection maps?
Decision Transformers for Wireless Communications: A New Paradigm of Resource Management
By leveraging the power of DT models learned over offline datasets, the proposed architecture is expected to achieve rapid convergence with many fewer training epochs and higher performance in new scenarios with different state and action spaces, compared with DRL.
Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-training
We leverage descriptions in medical reports to design multi-granular question-answer pairs associated with different diseases, which assist the framework in pre-training without requiring extra annotations from experts.
Diff-Reg v1: Diffusion Matching Model for Registration Problem
Establishing reliable correspondences is essential for registration tasks such as 3D and 2D3D registration.
Tri-Perspective View Decomposition for Geometry-Aware Depth Completion
Depth completion is a vital task for autonomous driving, as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements.
Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation
Although some approaches work towards gloss-free SLT through jointly training the visual encoder and translation network, these efforts still suffer from poor performance and inefficient use of the powerful Large Language Model (LLM).
Ranked #2 on
Gloss-free Sign Language Translation
on CSL-Daily
Harmonious Group Choreography with Trajectory-Controllable Diffusion
Specifically, to tackle dancer collisions, we introduce a Dance-Beat Navigator capable of generating trajectories for multiple dancers based on the music, complemented by a Distance-Consistency loss to maintain appropriate spacing among trajectories within a reasonable threshold.
Ranked #3 on
Motion Synthesis
on AIOZ-GDANCE
On the Effectiveness of Distillation in Mitigating Backdoors in Pre-trained Encoder
Distillation aims to distill knowledge from a given model (a. k. a the teacher net) and transfer it to another (a. k. a the student net).
Towards Safe and Reliable Autonomous Driving: Dynamic Occupancy Set Prediction
In the rapidly evolving field of autonomous driving, reliable prediction is pivotal for vehicular safety.
Performance Evaluation and Analysis of Thresholding-based Interference Mitigation for Automotive Radar Systems
In automotive radar, time-domain thresholding (TD-TH) and time-frequency domain thresholding (TFD-TH) are crucial techniques underpinning numerous interference mitigation methods.
Ploutos: Towards interpretable stock movement prediction with financial large language model
Recent advancements in large language models (LLMs) have opened new pathways for many domains.
Private Knowledge Sharing in Distributed Learning: A Survey
In this light, it is crucial to utilize information in learning processes that are either distributed or owned by different entities.
PVLR: Prompt-driven Visual-Linguistic Representation Learning for Multi-Label Image Recognition
In contrast to the unidirectional fusion in previous works, we introduce a Dual-Modal Attention (DMA) that enables bidirectional interaction between textual and visual features, yielding context-aware label representations and semantic-related visual representations, which are subsequently used to calculate similarities and generate final predictions for all labels.
SGV3D:Towards Scenario Generalization for Vision-based Roadside 3D Object Detection
Our method surpasses all previous methods by a significant margin in new scenes, including +42. 57% for vehicle, +5. 87% for pedestrian, and +14. 89% for cyclist compared to BEVHeight on the DAIR-V2X-I heterologous benchmark.
Exploring Multi-Modal Control in Music-Driven Dance Generation
Existing music-driven 3D dance generation methods mainly concentrate on high-quality dance generation, but lack sufficient control during the generation process.
Text2Avatar: Text to 3D Human Avatar Generation with Codebook-Driven Body Controllable Attribute
Generating 3D human models directly from text helps reduce the cost and time of character modeling.
Towards Balanced Alignment: Modal-Enhanced Semantic Modeling for Video Moment Retrieval
In this paper, we introduce Modal-Enhanced Semantic Modeling (MESM), a novel framework for more balanced alignment through enhancing features at two levels.
Compound Text-Guided Prompt Tuning via Image-Adaptive Cues
We introduce text supervision to the optimization of prompts, which enables two benefits: 1) releasing the model reliance on the pre-defined category names during inference, thereby enabling more flexible prompt generation; 2) reducing the number of inputs to the text encoder, which decreases GPU memory consumption significantly.
A General Neural Causal Model for Interactive Recommendation
Mitigation of the survivor bias is achieved though counterfactual consistency.
Adversarial Batch Inverse Reinforcement Learning: Learn to Reward from Imperfect Demonstration for Interactive Recommendation
In this research, we focus on the problem of learning to reward (LTR), which is fundamental to reinforcement learning.
Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in NMS
The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process.
FMRT: Learning Accurate Feature Matching with Reconciliatory Transformer
However, these methods only integrate long-range context information among keypoints with a fixed receptive field, which constrains the network from reconciling the importance of features with different receptive fields to realize complete image perception, hence limiting the matching accuracy.
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding
However, the domain discrepancy and data scarcity problems among clients deteriorate the performance of the global FL model.
Revisiting Multi-modal 3D Semantic Segmentation in Real-world Autonomous Driving
LiDAR and camera are two critical sensors for multi-modal 3D semantic segmentation and are supposed to be fused efficiently and robustly to promise safety in various real-world scenarios.
Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving
Compared with commonly used 3D radars, the latest 4D radars have precise vertical resolution and higher point cloud density, making it a highly promising sensor for autonomous driving in complex environmental perception.
Dynamic Appearance Particle Neural Radiance Field
In this work, we propose Dynamic Appearance Particle Neural Radiance Field (DAP-NeRF), which introduces particle-based representation to model the motions of visual elements in a dynamic 3D scene.
Affine Frequency Division Multiplexing With Index Modulation
In the proposed AFDM-IM scheme, the information bits are carried by the activation state of the subsymbols in discrete affine Fourier (DAF) domain in addition to the conventional constellation symbols.
TP2O: Creative Text Pair-to-Object Generation using Balance Swap-Sampling
Generating creative combinatorial objects from two seemingly unrelated object texts is a challenging task in text-to-image synthesis, often hindered by a focus on emulating existing data distributions.
MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings
We discover that most existing monocular 3D object detectors rely on the ego-vehicle prior assumption that the optical axis of the camera is parallel to the ground.
BEVHeight++: Toward Robust Visual Centric 3D Object Detection
In essence, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods.
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems
Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT).
TSSAT: Two-Stage Statistics-Aware Transformation for Artistic Style Transfer
To address this issue, we imitate the drawing process of humans and propose a Two-Stage Statistics-Aware Transformation (TSSAT) module, which first builds the global style foundation by aligning the global statistics of content and style features and then further enriches local style details by swapping the local statistics (instead of local features) in a patch-wise manner, significantly improving the stylization effects.
A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction
Along with the proliferation of electric vehicles (EVs), optimizing the use of EV charging space can significantly alleviate the growing load on intelligent transportation systems.
Create Your World: Lifelong Text-to-Image Diffusion
Text-to-image generative models can produce diverse high-quality images of concepts with a text prompt, which have demonstrated excellent ability in image generation, image translation, etc.
DMKD: Improving Feature-based Knowledge Distillation for Object Detection Via Dual Masking Augmentation
Recent mainstream masked distillation methods function by reconstructing selectively masked areas of a student network from the feature map of its teacher counterpart.
RigNet++: Semantic Assisted Repetitive Image Guided Network for Depth Completion
To tackle these challenges, we explore a repetitive design in our image guided network to gradually and sufficiently recover depth values.
Fragment and Integrate Network (FIN): A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction
(ii) Integrate Network (IN) builds a new integrated sequence by utilizing spatial-temporal interaction on MSS and captures the comprehensive spatial-temporal representation by modeling the integrated sequence with a complicated attention.
SkipcrossNets: Adaptive Skip-cross Fusion for Road Detection
Specifically, skip-cross fusion strategy connects each layer to each layer in a feed-forward manner, and for each layer, the feature maps of all previous layers are used as input and its own feature maps are used as input to all subsequent layers for the other modality, enhancing feature propagation and multi-modal features fusion.
AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization
Neural Radiance Fields (NeRF) have shown promise in generating realistic novel views from sparse scene images.
A One-dimensional HEVC video steganalysis method using the Optimality of Predicted Motion Vectors
And then, we define the optimal rate of MVP in HEVC video as a steganalysis feature.
Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis
In this paper, a dynamic dual-graph fusion convolutional network is proposed to improve Alzheimer's disease (AD) diagnosis performance.
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity
With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely considered for application in wireless networks for distributed model training.
Aspect based sentimental analysis for travellers' reviews
The results provide tangible reasons to use aspect based sentimental analysis in order to understand more the travellers and spot airport services that are in need for improvement.
UniAP: Unifying Inter- and Intra-Layer Automatic Parallelism by Mixed Integer Quadratic Programming
To the best of our knowledge, UniAP is the first parallel method that can jointly optimize the two categories of parallel strategies to find an optimal solution.
Creative Birds: Self-Supervised Single-View 3D Style Transfer
Our focus lies primarily on birds, a popular subject in 3D reconstruction, for which no existing single-view 3D transfer methods have been developed. The method we propose seeks to generate a 3D mesh shape and texture of a bird from two single-view images.
Bayesian Linear Regression with Cauchy Prior and Its Application in Sparse MIMO Radar
In this paper, a sparse signal recovery algorithm using Bayesian linear regression with Cauchy prior (BLRC) is proposed.
General vs. Long-Tailed Age Estimation: An Approach to Kill Two Birds with One Stone
Driven by this, some works suggest that each class should be treated equally to improve performance in tail classes (with a minority of samples), which can be summarized as Long-tailed Age Estimation.
Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning
As an efficient distributed machine learning approach, Federated learning (FL) can obtain a shared model by iterative local model training at the user side and global model aggregating at the central server side, thereby protecting privacy of users.
NCL++: Nested Collaborative Learning for Long-Tailed Visual Recognition
To achieve the collaborative learning in long-tailed learning, the balanced online distillation is proposed to force the consistent predictions among different experts and augmented copies, which reduces the learning uncertainties.
Learnable Differencing Center for Nighttime Depth Perception
Depth completion is the task of recovering dense depth maps from sparse ones, usually with the help of color images.
Variable Radiance Field for Real-Life Category-Specifc Reconstruction from Single Image
Our key contributions are: (1) We parameterize the geometry and appearance of the object using a multi-scale global feature extractor, which avoids frequent point-wise feature retrieval and camera dependency.
EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection
In face detection, low-resolution faces, such as numerous small faces of a human group in a crowded scene, are common in dense face prediction tasks.
Self-Aware Trajectory Prediction for Safe Autonomous Driving
Trajectory prediction is one of the key components of the autonomous driving software stack.
Path Planning for Air-Ground Robot Considering Modal Switching Point Optimization
A robot for the field application environment was proposed, and a lightweight global spatial planning technique for the robot based on the graph-search algorithm taking mode switching point optimization into account, with an emphasis on energy efficiency, searching speed, and the viability of real deployment.
Human Machine Co-adaption Interface via Cooperation Markov Decision Process System
Based on the proposed co-adaptive MDPs, the simulation study indicates the non-stationary problem can be mitigated using various proposed Policy Improvement approaches.
Model-based Reinforcement Learning
Multi-agent Reinforcement Learning
+2
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing
However, it suffers from issues in terms of communication, resource of MTs, and privacy.
ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform Discharges
Patient-independent detection of epileptic activities based on visual spectral representation of continuous EEG (cEEG) has been widely used for diagnosing epilepsy.
Informative Data Selection with Uncertainty for Multi-modal Object Detection
To quantify the correlation in multi-modal information, we model the uncertainty, as the inverse of data information, in different modalities and embed it in the bounding box generation.
Collaborative Multi-BS Power Management for Dense Radio Access Network using Deep Reinforcement Learning
Network energy efficiency is a main pillar in the design and operation of wireless communication systems.
Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy
Specifically, we first design a random sparsification algorithm to retain a fraction of the gradient elements in each client's local training, thereby mitigating the performance degradation induced by DP and and reducing the number of transmission parameters over wireless channels.
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning
In this paper, we design two-level incentive mechanisms for the HFL with a two-tiered computing structure to encourage the participation of entities in each tier in the HFL training.
LogoNet: a fine-grained network for instance-level logo sketch retrieval
To our knowledge, this is the first publicly available instance-level logo sketch dataset.
Boundary-to-Solution Mapping for Groundwater Flows in a Toth Basin
In this paper, the authors propose a new approach to solving the groundwater flow equation in the Toth basin of arbitrary top and bottom topographies using deep learning.
Pi-ViMo: Physiology-inspired Robust Vital Sign Monitoring using mmWave Radars
In this paper, we address these limitations and present "Pi-ViMo", a non-contact Physiology-inspired Robust Vital Sign Monitoring system, using mmWave radars.
Frozen Language Model Helps ECG Zero-Shot Learning
In downstream classification tasks, METS achieves around 10% improvement in performance without using any annotated data via zero-shot classification, compared to other supervised and SSL baselines that rely on annotated data.
BEVHeight: A Robust Framework for Vision-based Roadside 3D Object Detection
In essence, instead of predicting the pixel-wise depth, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods.
Ranked #3 on
3D Object Detection
on Rope3D
O2CTA: Introducing Annotations from OCT to CCTA in Coronary Plaque Analysis
Therefore, it is clinically critical to introduce annotations of plaque tissue and lumen characteristics from OCT to paired CCTA scans, denoted as \textbf{the O2CTA problem} in this paper.
Non-aligned supervision for Real Image Dehazing
In particular, we explore a non-alignment scenario that a clear reference image, unaligned with the input hazy image, is utilized to supervise the dehazing network.
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning
The contribution of the new DP mechanism to the convergence and accuracy of privacy-preserving FL is corroborated, compared to the state-of-the-art Gaussian noise mechanism with a persistent noise amplitude.
EfficientFace: An Efficient Deep Network with Feature Enhancement for Accurate Face Detection
Besides, this is conducive to estimating the locations of faces and enhancing the descriptive power of face features.
InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation
Moreover, they cannot explore which 3D geometric characteristics are essential to alleviate the catastrophic forgetting on old classes of 3D objects.
Recurrent Structure Attention Guidance for Depth Super-Resolution
Second, instead of the coarse concatenation guidance, we propose a recurrent structure attention block, which iteratively utilizes the latest depth estimation and the image features to jointly select clear patterns and boundaries, aiming at providing refined guidance for accurate depth recovery.