Search Results for author:
Found 175 papers, 61 papers with code
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing?
Cross-lingual Entity Typing (CLET) aims at improving the quality of entity type prediction by transferring semantic knowledge learned from rich-resourced languages to low-resourced languages.
Ruleformer: Context-aware Rule Mining over Knowledge Graph
Based on this idea, we propose a transformer-based rule mining approach, Ruleformer.
MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research
Recent advancements in AI agents have demonstrated their growing potential to drive and support scientific discovery.
AdaTP: Attention-Debiased Token Pruning for Video Large Language Models
However, these scores exhibit inherent biases: global bias reflects a tendency to focus on the two ends of the visual token sequence, while local bias leads to an over-concentration on the same spatial positions across different frames.
RIS Beam Calibration for ISAC Systems: Modeling and Performance Analysis
This highlights the necessity of realistic beam modeling and calibration to achieve high-accuracy localization.
Near-Field RIS-Assisted Localization Under Mutual Coupling
Reconfigurable intelligent surfaces (RISs) have the potential to significantly enhance the performance of integrated sensing and communication (ISAC) systems, particularly in line-of-sight (LoS) blockage scenarios.
Cross-layer Integrated Sensing and Communication: A Joint Industrial and Academic Perspective
Integrated sensing and communication (ISAC) enables radio systems to simultaneously sense and communicate with their environment.
Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks
Accurate and robust localization is a critical enabler for emerging 5G and 6G applications, including autonomous driving, extended reality (XR), and smart manufacturing.
Pilot-Based End-to-End Radio Positioning and Mapping for ISAC: Beyond Point-Based Landmarks
Integrated sensing and communication enables simultaneous communication and sensing tasks, including precise radio positioning and mapping, essential for future 6G networks.
PREMISE: Matching-based Prediction for Accurate Review Recommendation
We present PREMISE (PREdict with Matching ScorEs), a new architecture for the matching-based learning in the multimodal fields for the multimodal review helpfulness (MRHP) task.
Distributed Intelligent Sensing and Communications for 6G: Architecture and Use Cases
The Distributed Intelligent Sensing and Communication (DISAC) framework redefines Integrated Sensing and Communication (ISAC) for 6G by leveraging distributed architectures to enhance scalability, adaptability, and resource efficiency.
Cross-Lingual Consistency: A Novel Inference Framework for Advancing Reasoning in Large Language Models
Chain-of-thought (CoT) has emerged as a critical mechanism for enhancing reasoning capabilities in large language models (LLMs), with self-consistency demonstrating notable promise in boosting performance.
LSNet: See Large, Focus Small
Vision network designs, including Convolutional Neural Networks and Vision Transformers, have significantly advanced the field of computer vision.
Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing
Experiments on simulated and real hyperspectral data indicate that the proposed method delivers better unmixing results.
DiffusionTalker: Efficient and Compact Speech-Driven 3D Talking Head via Personalizer-Guided Distillation
We further propose a personalizer enhancer during distillation to enhance the influence of embeddings on facial animation.
YOLOE: Real-Time Seeing Anything
Object detection and segmentation are widely employed in computer vision applications, yet conventional models like YOLO series, while efficient and accurate, are limited by predefined categories, hindering adaptability in open scenarios.
Joint Bistatic Positioning and Monostatic Sensing: Optimized Beamforming and Performance Tradeoff
To adapt to low-cost hardwares, we develop unit-amplitude analog FDB and CPA schemes based on the weighted-sum mismatch of the covariance matrices paradigm, solved using distinct methods.
A Dynamic Model of Private Asset Allocation
We build a state-of-the-art dynamic model of private asset allocation that considers five key features of private asset markets: (1) the illiquid nature of private assets, (2) timing lags between capital commitments, capital calls, and eventual distributions, (3) time-varying business cycle conditions, (4) serial correlation in observed private asset returns, and (5) regulatory constraints on certain institutional investors' portfolio choices.
Joint Near-Field Sensing and Visibility Region Detection with Extremely Large Aperture Arrays
In this paper, we consider near-field localization and sensing with an extremely large aperture array under partial blockage of array antennas, where spherical wavefront and spatial non-stationarity are accounted for.
RIS-Aided Positioning Under Adverse Conditions: Interference from Unauthorized RIS
Positioning technology, which aims to determine the geometric information of a device in a global coordinate, is a key component in integrated sensing and communication systems.
Intrinsic Cramér-Rao Bound based 6D Localization and Tracking for 5G/6G Systems
Localization and tracking are critical components of integrated sensing and communication (ISAC) systems, enhancing resource management, beamforming accuracy, and overall system reliability through precise sensing.
DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs
As large language models continue to scale, computational costs and resource consumption have emerged as significant challenges.
Finedeep: Mitigating Sparse Activation in Dense LLMs via Multi-Layer Fine-Grained Experts
Moreover, we find that Finedeep achieves optimal results when balancing depth and width, specifically by adjusting the number of expert sub-layers and the number of experts per sub-layer.
Modern Base Station Architecture: Enabling Passive Beamforming with Beyond Diagonal RISs
Beamforming plays a crucial role in millimeter wave (mmWave) communication systems to mitigate the severe attenuation inherent to this spectrum.
Integrated 6G TN and NTN Localization: Challenges, Opportunities, and Advancements
The rapid evolution of cellular networks has introduced groundbreaking technologies, including large and distributed antenna arrays and reconfigurable intelligent surfaces in terrestrial networks (TNs), as well as aerial and space-based nodes in non-terrestrial networks (NTNs).
Optimized Beamforming for Joint Bistatic Positioning and Monostatic Sensing
We investigate the performance tradeoff between \textit{bistatic positioning (BP)} and \textit{monostatic sensing (MS)} in a multi-input multi-output orthogonal frequency division multiplexing scenario.
Privacy Preservation in MIMO-OFDM Localization Systems: A Beamforming Approach
We investigate an uplink MIMO-OFDM localization scenario where a legitimate base station (BS) aims to localize a user equipment (UE) using pilot signals transmitted by the UE, while an unauthorized BS attempts to localize the UE by eavesdropping on these pilots, posing a risk to the UE's location privacy.
YOLO-UniOW: Efficient Universal Open-World Object Detection
In this work, we introduce Universal Open-World Object Detection (Uni-OWD), a new paradigm that unifies open-vocabulary and open-world object detection tasks.
GraphAvatar: Compact Head Avatars with GNN-Generated 3D Gaussians
In this paper, we introduce a method called GraphAvatar that utilizes Graph Neural Networks (GNN) to generate 3D Gaussians for the head avatar.
Residual Channel Boosts Contrastive Learning for Radio Frequency Fingerprint Identification
In order to address the issue of limited data samples for the deployment of pre-trained models in unseen environments, this paper proposes a residual channel-based data augmentation strategy for Radio Frequency Fingerprint Identification (RFFI), coupled with a lightweight SimSiam contrastive learning framework.
Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models
In this paper, we introduce a novel single-stage continual pretraining method, Head-Adaptive Rotary Position Encoding (HARPE), to equip LLMs with long context modeling capabilities while simplifying the training process.
[CLS] Token Tells Everything Needed for Training-free Efficient MLLMs
In this paper, we demonstrate that in MLLMs, the [CLS] token in the visual encoder inherently knows which visual tokens are important for MLLMs.
PrefixKV: Adaptive Prefix KV Cache is What Vision Instruction-Following Models Need for Efficient Generation
With an adaptive layer-wise KV retention recipe based on binary search, the maximum contextual information can thus be preserved in each layer, facilitating the generation.
Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning
Segment Anything Model (SAM) has made great progress in anomaly segmentation tasks due to its impressive generalization ability.
Efficient Localization with Base Station-Integrated Beyond Diagonal RIS
This paper introduces a novel approach to efficient localization in next-generation communication systems through a base station (BS)-enabled passive beamforming utilizing beyond diagonal reconfigurable intelligent surfaces (BD-RISs).
GazeGaussian: High-Fidelity Gaze Redirection with 3D Gaussian Splatting
Comprehensive experiments show that GazeGaussian outperforms existing methods in rendering speed, gaze redirection accuracy, and facial synthesis across multiple datasets.
Structured Dialogue System for Mental Health: An LLM Chatbot Leveraging the PM+ Guidelines
The Structured Dialogue System, referred to as SuDoSys, is an innovative Large Language Model (LLM)-based chatbot designed to provide psychological counseling.
LBPE: Long-token-first Tokenization to Improve Large Language Models
The prevalent use of Byte Pair Encoding (BPE) in Large Language Models (LLMs) facilitates robust handling of subword units and avoids issues of out-of-vocabulary words.
Target Handover in Distributed Integrated Sensing and Communication
This paper addresses the target handover challenge in DISAC systems and introduces a method enabling BSs to share essential target trajectory information at appropriate time steps, facilitating seamless handovers to other BSs.
Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision
Specifically, we discuss recent efficient deep learning infrastructures for embedded computing systems from the lens of (1) efficient manual network design for embedded computing systems, (2) efficient automated network design for embedded computing systems, (3) efficient network compression for embedded computing systems, (4) efficient on-device learning for embedded computing systems, (5) efficient large language models for embedded computing systems, (6) efficient deep learning software and hardware for embedded computing systems, and (7) efficient intelligent applications for embedded computing systems.
Marked Temporal Bayesian Flow Point Processes
Additionally, by adding joint noise to the marked temporal data space, BMTPP effectively captures and explicitly reveals the interdependence between timestamps and event types.
MM-WLAuslan: Multi-View Multi-Modal Word-Level Australian Sign Language Recognition Dataset
Experiment results indicate that MM-WLAuslan is a challenging ISLR dataset, and we hope this dataset will contribute to the development of Auslan and the advancement of sign languages worldwide.
CartesianMoE: Boosting Knowledge Sharing among Experts via Cartesian Product Routing in Mixture-of-Experts
Large language models (LLM) have been attracting much attention from the community recently, due to their remarkable performance in all kinds of downstream tasks.
Federated Neural Nonparametric Point Processes
Temporal point processes (TPPs) are effective for modeling event occurrences over time, but they struggle with sparse and uncertain events in federated systems, where privacy is a major concern.
Calibration in RIS-aided Integrated Sensing, Localization and Communication Systems
Reconfigurable intelligent surfaces (RISs) are key enablers for integrated sensing and communication (ISAC) systems in the 6G communication era.
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection
By incorporating the RASFormer block, our RAS method achieves superior contextual awareness capabilities, leading to remarkable performance.
Multi-class Anomaly Detection
Unsupervised Anomaly Detection
When 3D Partial Points Meets SAM: Tooth Point Cloud Segmentation with Sparse Labels
To demonstrate the effectiveness of the framework, we conduct experiments on the public dataset and surprisingly find with only 0. 1\% annotations (one point per tooth), our method can surpass recent weakly supervised methods by a large margin, and the performance is even comparable to the recent fully-supervised methods, showcasing the significant potential of applying SAM to 3D perception tasks with sparse labels.
Privacy Preservation in Delay-Based Localization Systems: Artificial Noise or Artificial Multipath?
Localization plays an increasingly pivotal role in 5G/6G systems, enabling various applications.
LLMI3D: Empowering LLM with 3D Perception from a Single 2D Image
Recent advancements in autonomous driving, augmented reality, robotics, and embodied intelligence have necessitated 3D perception algorithms.
Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence
In response, we present learn from the learnt (LFTL), a novel paradigm for SFADA to leverage the learnt knowledge from the source pretrained model and actively iterated models without extra overhead.
Multipath Identification and Mitigation with FDA-MIMO Radar
In smart city development, the automatic detection of structures and vehicles within urban or suburban areas via array radar (airborne or vehicle platforms) becomes crucial.
Quantized Prompt for Efficient Generalization of Vision-Language Models
During downstream adaptation, the most challenging problems are overfitting and catastrophic forgetting, which can cause the model to overly focus on the current data and lose more crucial domain-general knowledge.
MaskMoE: Boosting Token-Level Learning via Routing Mask in Mixture-of-Experts
Currently, the mainstream routing methods include dynamic routing and fixed routing.
AoA-Based Physical Layer Authentication in Analog Arrays under Impersonation Attacks
We discuss the use of angle of arrival (AoA) as an authentication measure in analog array multiple-input multiple-output (MIMO) systems.
V2X Sidelink Positioning in FR1: From Ray-Tracing and Channel Estimation to Bayesian Tracking
Sidelink positioning research predominantly focuses on the snapshot positioning problem, often within the mmWave band.
Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition
Disordered speech recognition profound implications for improving the quality of life for individuals afflicted with, for example, dysarthria.
Ghost imaging-based Non-contact Heart Rate Detection
Remote heart rate measurement is an increasingly concerned research field, usually using remote photoplethysmography (rPPG) to collect heart rate information through video data collection.
ParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow Networks
Specifically, ParamReL proposes a \emph{self-}encoder to learn latent semantics directly from parameters, rather than from observations.
YOLOv10: Real-Time End-to-End Object Detection
In this work, we aim to further advance the performance-efficiency boundary of YOLOs from both the post-processing and model architecture.
Ranked #23 on
Real-Time Object Detection
on MS COCO
More is Better: Deep Domain Adaptation with Multiple Sources
Domain adaptation (DA) aims to address this problem by aligning the distributions between the source and target domains.
Scaffold-BPE: Enhancing Byte Pair Encoding for Large Language Models with Simple and Effective Scaffold Token Removal
Since BPE iteratively merges the most frequent token pair in the text corpus to generate a new token and keeps all generated tokens in the vocabulary, it unavoidably holds tokens that primarily act as components of a longer token and appear infrequently on their own.
Temporal Scaling Law for Large Language Models
In this paper, we propose the novel concept of Temporal Scaling Law, studying how the test loss of an LLM evolves as the training steps scale up.
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification
While deep neural networks (DNNs) based personalized federated learning (PFL) is demanding for addressing data heterogeneity and shows promising performance, existing methods for federated learning (FL) suffer from efficient systematic uncertainty quantification.
Device-Free 3D Drone Localization in RIS-Assisted mmWave MIMO Networks
In this paper, we investigate the potential of reconfigurable intelligent surfaces (RISs) in facilitating passive/device-free three-dimensional (3D) drone localization within existing cellular infrastructure operating at millimeter-wave (mmWave) frequencies and employing multiple antennas at the transceivers.
PYRA: Parallel Yielding Re-Activation for Training-Inference Efficient Task Adaptation
Consequently, a simple combination of them cannot guarantee accomplishing both training efficiency and inference efficiency with minimal costs.
Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
Data users have been endowed with the right to be forgotten of their data.
ELAA Near-Field Localization and Sensing with Partial Blockage Detection
Considering the array size could be much larger than an obstacle, the effect of partial blockage (i. e., a portion of antennas are blocked) is investigated, and a blockage detection algorithm is proposed.
Towards Robust Instruction Tuning on Multimodal Large Language Models
Fine-tuning large language models (LLMs) on multi-task instruction-following data has been proven to be a powerful learning paradigm for improving their zero-shot capabilities on new tasks.
Bring Your Own Character: A Holistic Solution for Automatic Facial Animation Generation of Customized Characters
In our solution, a deep learning model was first trained to retarget the facial expression from input face images to virtual human faces by estimating the blendshape coefficients.
Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection
Due to their unsupervised training and uncertainty estimation, deep Variational Autoencoders (VAEs) have become powerful tools for reconstruction-based Time Series Anomaly Detection (TSAD).
One-dimensional Adapter to Rule Them All: Concepts Diffusion Models and Erasing Applications
The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors.
One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications
The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors.
On Smart Morphing Wing Aircraft Robust Adaptive Beamforming
To overcome this problem and ensure robust beamforming for FCA, deviations in array control parameters (ACPs) and array perturbations, the effect of mutual coupling in addition to looking-direction errors should be considered.
Towards Efficient Vision-Language Tuning: More Information Density, More Generalizability
To better understand the nature of prompt tuning, we propose the concept of ``Information Density'' (ID) to indicate whether a matrix strongly belongs to certain feature spaces rather than being evenly distributed across various feature spaces.
RepViT-SAM: Towards Real-Time Segmenting Anything
Here, to achieve real-time segmenting anything on mobile devices, following MobileSAM, we replace the heavyweight image encoder in SAM with RepViT model, ending up with the RepViT-SAM model.
DiffusionTalker: Personalization and Acceleration for Speech-Driven 3D Face Diffuser
To address the above limitations, we propose DiffusionTalker, a diffusion-based method that utilizes contrastive learning to personalize 3D facial animation and knowledge distillation to accelerate 3D animation generation.
RIS Position and Orientation Estimation via Multi-Carrier Transmissions and Multiple Receivers
In this paper, we consider a static user equipped with an RIS and study the RIS localization problem (i. e., joint three-dimensional position and orientation estimation), when operating in a system comprising a single-antenna transmitter and multiple synchronized single-antenna receivers with known locations.
InstructCoder: Instruction Tuning Large Language Models for Code Editing
In this work, we explore the use of Large Language Models (LLMs) to edit code based on user instructions.
MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language Models
Experiments reveal that models incorporating the proposed MiLe Loss can gain consistent performance improvement on downstream benchmarks.
Ranked #42 on
Multi-task Language Understanding
on MML
Confidence-based Visual Dispersal for Few-shot Unsupervised Domain Adaptation
To address this issue, recent works consider the Few-shot Unsupervised Domain Adaptation (FUDA) where only a few source samples are labeled, and conduct knowledge transfer via self-supervised learning methods.
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations
BPFL aims to quantify the uncertainty and heterogeneity within and across clients towards uncertainty representations by addressing the statistical heterogeneity of client data.
Missing-modality Enabled Multi-modal Fusion Architecture for Medical Data
This study aimed to develop an efficient multi-modal fusion architecture for medical data that was robust to missing modalities and further improved the performance on disease diagnosis. X-ray chest radiographs for the image modality, radiology reports for the text modality, and structured value data for the tabular data modality were fused in this study.
CAIT: Triple-Win Compression towards High Accuracy, Fast Inference, and Favorable Transferability For ViTs
We further employ a consistent dynamic channel pruning (CDCP) strategy to dynamically prune unimportant channels in ViTs.
6G Positioning and Sensing Through the Lens of Sustainability, Inclusiveness, and Trustworthiness
6G promises a paradigm shift by integrating positioning and sensing, enhancing not only the communication performance but also enabling location- and context-aware services.
Application of Visual Communication in Image Enhancement and Optimization of Human–Computer Interface
The system requires the shortest time and has the best performance for processing color images.
RepViT: Revisiting Mobile CNN From ViT Perspective
Recently, lightweight Vision Transformers (ViTs) demonstrate superior performance and lower latency, compared with lightweight Convolutional Neural Networks (CNNs), on resource-constrained mobile devices.
Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models
Video question-answering is a fundamental task in the field of video understanding.
Ranked #10 on
TGIF-Frame
on TGIF-QA
CodeInstruct: Empowering Language Models to Edit Code
In this work, we explore the use of large language models (LLMs) to edit code based on user instructions, covering a broad range of implicit tasks such as comment insertion, code optimization, and code refactoring.
Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction
Through comprehensive experiments involving multiple tasks, settings, and models, we demonstrate that CASE can serve as a general decoding strategy for complex sentiment tasks.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
Zero Access Points 3D Cooperative Positioning via RIS and Sidelink Communications
Reconfigurable intelligent surfaces (RISs) are expected to be a main component of future 6G networks, due to their capability to create a controllable wireless environment, and achieve extended coverage and improved localization accuracy.
Consolidator: Mergeable Adapter with Grouped Connections for Visual Adaptation
To further enhance the model's capacity to transfer knowledge under a constrained storage budget and keep inference efficient, we consolidate the parameters in two stages: 1. between adaptation and storage, and 2. between loading and inference.
Bayesian Federated Learning: A Survey
This survey presents a critical overview of BFL, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of BFL from both Bayesian and federated perspectives.
JrCUP: Joint RIS Calibration and User Positioning for 6G Wireless Systems
In the second stage, a two-dimensional search algorithm is proposed to estimate the three-dimensional user and RIS positions, one-dimensional RIS orientation, and clock bias from the estimated channel parameters.
Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels
In this paper, we advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior about the label-to-label correspondence via a semantic prior prompter.
Box-Level Active Detection
Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection.
Experimental Validation of Single BS 5G mmWave Positioning and Mapping for Intelligent Transport
Positioning with 5G signals generally requires connection to several base stations (BSs), which makes positioning more demanding in terms of infrastructure than communications.
A Surrogate-Assisted Highly Cooperative Coevolutionary Algorithm for Hyperparameter Optimization in Deep Convolutional Neural Network
Convolutional neural networks (CNNs) have gained remarkable success in recent years.
Multi-RIS-Enabled 3D Sidelink Positioning
Positioning is expected to be a core function in intelligent transportation systems (ITSs) to support communication and location-based services, such as autonomous driving, traffic control, etc.
MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferring
However, it is challenging to develop a single-lead-based ECG interpretation model for multiple diseases diagnosis due to the lack of some key disease information.
RISs and Sidelink Communications in Smart Cities: The Key to Seamless Localization and Sensing
A smart city involves, among other elements, intelligent transportation, crowd monitoring, and digital twins, each of which requires information exchange via wireless communication links and localization of connected devices and passive objects (including people).
Modeling and Analysis of OFDM-based 5G/6G Localization under Hardware Impairments
Our extensive simulation results demonstrate that each type of HWI leads to a different level of degradation in angle and delay estimation performance, and the prominent impairment factors on delay estimation will have a dominant negative effect on SER.
RIS-Aided Monostatic Sensing and Object Detection with Single and Double Bounce Multipath
We propose a framework for monostatic sensing by a user equipment (UE), aided by a reconfigurable intelligent surface (RIS) in environments with single- and double-bounce signal propagation.
RIS-Enabled and Access-Point-Free Simultaneous Radio Localization and Mapping
In the upcoming sixth generation (6G) of wireless communication systems, reconfigurable intelligent surfaces~(RISs) are regarded as one of the promising technological enablers, which can provide programmable signal propagation.
Joint RIS Calibration and Multi-User Positioning
Reconfigurable intelligent surfaces (RISs) are expected to be a key component enabling the mobile network evolution towards a flexible and intelligent 6G wireless platform.
Unsupervised Model-based speaker adaptation of end-to-end lattice-free MMI model for speech recognition
Modeling the speaker variability is a key challenge for automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Misspecified Cramér-Rao Bound of RIS-aided Localization under Geometry Mismatch
In 5G/6G wireless systems, reconfigurable intelligent surfaces (RIS) can play a role as a passive anchor to enable and enhance localization in various scenarios.
Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection
To pick up the ground plane prior for M3OD, we propose a Ground Plane Enhanced Network (GPENet) which resolves both issues at one go.
Positioning and Sensing in 6G: Gaps, Challenges, and Opportunities
Among the key differentiators of 6G compared to 5G will be the increased emphasis on radio based positioning and sensing.
MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences
Existing multimodal tasks mostly target at the complete input modality setting, i. e., each modality is either complete or completely missing in both training and test sets.
SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training
Self-training methods have been explored in recent years and have exhibited great performance in improving semi-supervised learning.
Generative Knowledge Graph Construction: A Review
Our contributions are threefold: (1) We present a detailed, complete taxonomy for the generative KGC methods; (2) We provide a theoretical and empirical analysis of the generative KGC methods; (3) We propose several research directions that can be developed in the future.
Joint User Localization and Location Calibration of A Hybrid Reconfigurable Intelligent Surface
The recent research in the emerging technology of reconfigurable intelligent surfaces (RISs) has identified its high potential for localization and sensing.
Neural-Symbolic Entangled Framework for Complex Query Answering
In this work, we propose a Neural and Symbolic Entangled framework (ENeSy) for complex query answering, which enables the neural and symbolic reasoning to enhance each other to alleviate the cascading error and KG incompleteness.
Localization Coverage Analysis of THz Communication Systems with a 3D Array
This paper considers the problem of estimating the position and orientation of a user equipped with a three-dimensional (3D) array receiving downlink far-field THz signals from multiple base stations with known positions and orientations.
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning
With the boom of e-commerce, Multimodal Review Helpfulness Prediction (MRHP), which aims to sort product reviews according to the predicted helpfulness scores has become a research hotspot.
DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification
This paper proposes a simple yet effective interpolation-based data augmentation approach termed DoubleMix, to improve the robustness of models in text classification.
Detecting Network-based Internet Censorship via Latent Feature Representation Learning
To estimate the probability of censorship events from the inferred latent features, we rely on a densely connected multi-layer neural network model.
You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms
Benefiting from the search efficiency, differentiable neural architecture search (NAS) has evolved as the most dominant alternative to automatically design competitive deep neural networks (DNNs).
Doppler Exploitation in Bistatic mmWave Radio SLAM
Networks in 5G and beyond utilize millimeter wave (mmWave) radio signals, large bandwidths, and large antenna arrays, which bring opportunities in jointly localizing the user equipment and mapping the propagation environment, termed as simultaneous localization and mapping (SLAM).
Bi-Static Sensing for Near-Field RIS Localization
Due to the deployment of RISs with a large dimension, near-field (NF) scenarios are likely to occur, especially for indoor applications, and are the focus of this work.
Channel Model Mismatch Analysis for XL-MIMO Systems from a Localization Perspective
Radio localization is applied in high-frequency (e. g., mmWave and THz) systems to support communication and to provide location-based services without extra infrastructure.
Doppler-Enabled Single-Antenna Localization and Mapping Without Synchronization
With the help of multipath components (MPCs), localization and mapping tasks can be done with a single base station (BS) and single unsynchronized user equipment (UE) if both of them are equipped with an antenna array.
Antenna Selection in Switch-Based MIMO Arrays via DOA threshold region Approximation
We first perform a subarray layout alignment process to remove subarrays with identical beampatterns and create a unique subarray set.
6G Radio Requirements to Support Integrated Communication, Localization, and Sensing
6G will be characterized by extreme use cases, not only for communication, but also for localization, and sensing.
Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce
In addition to formulating the new task, we also release a new Benchmark dataset of Salience Evaluation in E-commerce (BSEE) and hope to promote related research on commonsense knowledge salience evaluation.
Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction
Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching.
MCRB-based Performance Analysis of 6G Localization under Hardware Impairments
Location information is expected to be the key to meeting the needs of communication and context-aware services in 6G systems.
Quantity over Quality: Training an AV Motion Planner with Large Scale Commodity Vision Data
This motivates the use of commodity sensors like cameras for data collection, which are an order of magnitude cheaper than HD sensor suites, but offer lower fidelity.
From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer
Knowledge graph completion aims to address the problem of extending a KG with missing triples.
Ranked #52 on
Link Prediction
on FB15k-237
Ontology-enhanced Prompt-tuning for Few-shot Learning
Specifically, we develop the ontology transformation based on the external knowledge graph to address the knowledge missing issue, which fulfills and converts structure knowledge to text.
FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks
Quantization is applied to reduce the latency and storage cost of CNNs.
Constrained Wrapped Least Squares: A Tool for High Accuracy GNSS Attitude Determination
Attitude determination is a popular application of Global Navigation Satellite Systems (GNSS).
Ghost Imaging Based on Recurrent Neural Network
The testing results show that the proposed method can achieve image reconstruction at a very low sampling rate (0. 38$\%$).
A Tutorial on Terahertz-Band Localization for 6G Communication Systems
Terahertz (THz) communications are celebrated as key enablers for converged localization and sensing in future sixth-generation (6G) wireless communication systems and beyond.
Multi-trends Enhanced Dynamic Micro-video Recommendation
In this paper, we propose the DMR framework to explicitly model dynamic multi-trends of users' current preference and make predictions based on both the history and future potential trends.
Learning to Ask for Data-Efficient Event Argument Extraction
Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles.
Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis
In this work, we propose a framework named MultiModal InfoMax (MMIM), which hierarchically maximizes the Mutual Information (MI) in unimodal input pairs (inter-modality) and between multimodal fusion result and unimodal input in order to maintain task-related information through multimodal fusion.
Ranked #7 on
Multimodal Sentiment Analysis
on CMU-MOSI
Knowledge Perceived Multi-modal Pretraining in E-commerce
In this paper, we address multi-modal pretraining of product data in the field of E-commerce.
0.8% Nyquist computational ghost imaging via non-experimental deep learning
We present a framework for computational ghost imaging based on deep learning and customized pink noise speckle patterns.
Ranked #1 on
on VGG Face
(using extra training data)
Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis
Multimodal sentiment analysis aims to extract and integrate semantic information collected from multiple modalities to recognize the expressed emotions and sentiment in multimodal data.
A Game-Theoretic Approach to Multi-Agent Trust Region Optimization
Trust region methods are widely applied in single-agent reinforcement learning problems due to their monotonic performance-improvement guarantee at every iteration.
AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search.
OntoED: Low-resource Event Detection with Ontology Embedding
Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types.
Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph
We first construct a billion-scale conversation knowledge graph (CKG) from information about users, items and conversations, and then pretrain CKG by introducing knowledge graph embedding method and graph convolution network to encode semantic and structural information respectively. To make the CTR prediction model sensible of current state of users and the relationship between dialogues and items, we introduce user-state and dialogue-interaction representations based on pre-trained CKG and propose K-DCN. In K-DCN, we fuse the user-state representation, dialogue-interaction representation and other normal feature representations via deep cross network, which will give the rank of candidate items to be recommended. We experimentally prove that our proposal significantly outperforms baselines and show it's real application in Alime.
Click-Through Rate Prediction
Conversational Recommendation
+3
Speckles-Training-Based Denoising Convolutional Neural Network Ghost Imaging
Ghost imaging (GI) has been paid attention gradually because of its lens-less imaging capability, turbulence-free imaging and high detection sensitivity.
Text-guided Legal Knowledge Graph Reasoning
In this paper, we propose a novel legal application of legal provision prediction (LPP), which aims to predict the related legal provisions of affairs.
Generative-Adversarial-Networks-based Ghost Recognition
The proposed method can improve the problems caused by conventional recognition methods that based on target image information, and provide a certain turbulence-free ability.
Learning to Shape Rewards using a Game of Two Partners
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards.
Deep Structural Estimation: With an Application to Option Pricing
We propose a novel structural estimation framework in which we train a surrogate of an economic model with deep neural networks.
DOA Estimation with Non-Uniform Linear Arrays: A Phase-Difference Projection Approach
The concept of \emph{wrapped phased-difference pattern} (WPDP) is introduced, which allows the proposed algorithm to compute most of the parameters required for DOA estimation in an offline manner, hence resulting in a superior computational speed in realtime.
Contrastive Coding for Active Learning Under Class Distribution Mismatch
However, its performance deteriorates under class distribution mismatch, wherein the unlabeled data contain many samples out of the class distribution of labeled data.
Multi-Agent Trust Region Learning
We derive the lower bound of agents' payoff improvements for MATRL methods, and also prove the convergence of our method on the meta-game fixed points.
Physical rendering of synthetic spaces for topological sound transport
Synthetic dimensions can be rendered in the physical space and this has been achieved with photonics and cold atomic gases, however, little to no work has been succeeded in acoustics because acoustic wave-guides cannot be weakly coupled in a continuous fashion.
Mesoscale and Nanoscale Physics Classical Physics
Order of magnitude increase in laser-target coupling at near-relativistic intensities using compound parabolic concentrators
Achieving a high conversion efficiency into relativistic electrons is central to short-pulse laser application and fundamentally relies on creating interaction regions with intensities ${\gg}10^{18}$~W/cm$^2$.
Plasma Physics
Persuasive Dialogue Understanding: the Baselines and Negative Results
Persuasion aims at forming one's opinion and action via a series of persuasive messages containing persuader's strategies.
Learning to Infer Unseen Attribute-Object Compositions
The composition recognition of unseen attribute-object is critical to make machines learn to decompose and compose complex concepts like people.
Decision-Aware Conditional GANs for Time Series Data
We introduce the decision-aware time-series conditional generative adversarial network (DAT-CGAN) as a method for time-series generation.
DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning
In DualDE, we propose a soft label evaluation mechanism to adaptively assign different soft label and hard label weights to different triples, and a two-stage distillation approach to improve the student's acceptance of the teacher.
Dialogue Relation Extraction with Document-level Heterogeneous Graph Attention Networks
This graph is fed to a graph attention network for context propagation among relevant nodes, which effectively captures the dialogue context.
Ranked #7 on
Dialog Relation Extraction
on DialogRE
(F1c (v1) metric)
FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques and is a key step to improve the performance of machine learning algorithms.
A Derivation of Identifiable Condition for Non-Uniform Linear Array DOA Estimation
Phase ambiguity happens in uniform linear arrays (ULAs) when the sensor distance is greater than $\lambda/2$.
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
All methods were based on deep learning and categorized into two groups: multi-model method and single model method.
A Natural Language Processing Pipeline of Chinese Free-text Radiology Reports for Liver Cancer Diagnosis
Despite the rapid development of natural language processing (NLP) implementation in electronic medical records (EMRs), Chinese EMRs processing remains challenging due to the limited corpus and specific grammatical characteristics, especially for radiology reports.
IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
Existing methods leverage the attention mechanism to explore such correspondence in a fine-grained manner.
Ranked #20 on
Cross-Modal Retrieval
on Flickr30k
BEYOND SUPERVISED LEARNING: RECOGNIZING UNSEEN ATTRIBUTE-OBJECT PAIRS WITH VISION-LANGUAGE FUSION AND ATTRACTOR NETWORKS
This paper handles a challenging problem, unseen attribute-object pair recognition, which asks a model to simultaneously recognize the attribute type and the object type of a given image while this attribute-object pair is not included in the training set.
An Iterative Polishing Framework based on Quality Aware Masked Language Model for Chinese Poetry Generation
In this paper, we propose a novel iterative polishing framework for highly qualified Chinese poetry generation.
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER).
Ranked #1 on
Cross-Lingual NER
on MSRA
UER: An Open-Source Toolkit for Pre-training Models
Existing works, including ELMO and BERT, have revealed the importance of pre-training for NLP tasks.
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
By optimizing the PCR loss, PDANet can generate a polarity preserved attention map and thus improve the emotion regression performance.
GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition
The dominant approaches for named entity recognition (NER) mostly adopt complex recurrent neural networks (RNN), e. g., long-short-term-memory (LSTM).
Ranked #24 on
Named Entity Recognition (NER)
on Ontonotes v5 (English)
A new Majorana platform in an Fe-As bilayer superconductor
On the other hand, some iron-pnictide (Fe-As) superconductors, such as LiFeAs, have been demonstrated to have a similar topological band structure, yet no MZM has been observed in its vortex cores, raising a question of universality of MZM presence in iron-based superconductors.
Superconductivity Strongly Correlated Electrons
A Variational Approach for Learning from Positive and Unlabeled Data
Learning binary classifiers only from positive and unlabeled (PU) data is an important and challenging task in many real-world applications, including web text classification, disease gene identification and fraud detection, where negative samples are difficult to verify experimentally.
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome.
Geometry-Contrastive GAN for Facial Expression Transfer
Geometry information is introduced into cGANs as continuous conditions to guide the generation of facial expressions.
3D Model-Based Continuous Emotion Recognition
We propose a real-time 3D model-based method that continuously recognizes dimensional emotions from facial expressions in natural communications.
