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Found 67 papers, 24 papers with code
Full-Time Monocular Road Detection Using Zero-Distribution Prior of Angle of Polarization
Zero-distribution prior embodies the zero-distribution of Angle of Polarization (AoP) of a road scene image, which provides a significant contrast between the road and the background.
Scheduling of Flexible Manufacturing Systems Based on Place-Timed Petri Nets and Basis Reachability Graphs
Scheduling is a key decision-making process to improve the performance of flexible manufacturing systems.
Latent Video Dataset Distillation
Dataset distillation has demonstrated remarkable effectiveness in high-compression scenarios for image datasets.
A Survey of AI Agent Protocols
We expect this work to serve as a practical reference for both researchers and engineers seeking to design, evaluate, or integrate robust communication infrastructures for intelligent agents.
Anti-Aesthetics: Protecting Facial Privacy against Customized Text-to-Image Synthesis
The rise of customized diffusion models has spurred a boom in personalized visual content creation, but also poses risks of malicious misuse, severely threatening personal privacy and copyright protection.
The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented Generation
By referencing this external knowledge, RAG effectively reduces the generation of factually incorrect content and addresses hallucination issues within LLMs.
Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability
OpenAI's multimodal GPT-4o has demonstrated remarkable capabilities in image generation and editing, yet its ability to achieve world knowledge-informed semantic synthesis--seamlessly integrating domain knowledge, contextual reasoning, and instruction adherence--remains unproven.
ArxivBench: Can LLMs Assist Researchers in Conducting Research?
In this study, we evaluate both proprietary and open-source LLMs on their ability to respond with relevant research papers and accurate links to articles hosted on the arXiv platform, based on high level prompts.
From G-Factor to A-Factor: Establishing a Psychometric Framework for AI Literacy
This research addresses the growing need to measure and understand AI literacy in the context of generative AI technologies.
Similarity-Guided Layer-Adaptive Vision Transformer for UAV Tracking
Based on this observation, we propose a similarity-guided layer adaptation approach to optimize the structure of ViTs.
ColorDynamic: Generalizable, Scalable, Real-time, End-to-end Local Planner for Unstructured and Dynamic Environments
Deep Reinforcement Learning (DRL) has demonstrated potential in addressing robotic local planning problems, yet its efficacy remains constrained in highly unstructured and dynamic environments.
Robust Tracking via Mamba-based Context-aware Token Learning
To alleviate the above issues, we propose a simple yet robust tracker that separates temporal information learning from appearance modeling and extracts temporal relations from a set of representative tokens rather than several images (or features).
Incremental Online Learning of Randomized Neural Network with Forward Regularization
To alleviate these drawbacks and achieve progressive immediate decision-making, we propose a novel Incremental Online Learning (IOL) process of Randomized Neural Networks (Randomized NN), a framework facilitating continuous improvements to Randomized NN performance in restrictive online scenarios.
Augmenting Minds or Automating Skills: The Differential Role of Human Capital in Generative AI's Impact on Creative Tasks
This framework elucidates how AI shifts the locus of creative advantage from specialized expertise to broader cognitive adaptability.
A Method for Constructing Wavelet Functions on the Real Number Field
The conditions corresponding to the higher order vanishing moment of the seed sequence wavelet function are also given in this article.
Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following
To address this gap, we introduce Multi-IF, a new benchmark designed to assess LLMs' proficiency in following multi-turn and multilingual instructions.
ELF-Gym: Evaluating Large Language Models Generated Features for Tabular Prediction
But despite this potential, evaluations thus far are primarily based on the end performance of a complete ML pipeline, providing limited insight into precisely how LLMs behave relative to human experts in feature engineering.
Agentic Information Retrieval
Information state refers to a particular information context that the user is right in within a dynamic environment, encompassing not only the acquired information items but also real-time user preferences, contextual factors, and decision-making processes.
A QoE-Aware Split Inference Accelerating Algorithm for NOMA-based Edge Intelligence
Thus, the model split inference is proposed to improve the performance of edge intelligence, in which the AI model is divided into different sub models and the resource-intensive sub model is offloaded to edge server wirelessly for reducing resource requirements and inference latency.
Orthogonal Mode Decomposition for Finite Discrete Signals
In this paper, an orthogonal mode decomposition method is proposed to decompose ffnite length real signals on both the real and imaginary axes of the complex plane.
Can Large Language Models Replace Human Subjects? A Large-Scale Replication of Scenario-Based Experiments in Psychology and Management
Artificial Intelligence (AI) is increasingly being integrated into scientific research, particularly in the social sciences, where understanding human behavior is critical.
Multi-Modal Dialogue State Tracking for Playing GuessWhich Game
GuessWhich is an engaging visual dialogue game that involves interaction between a Questioner Bot (QBot) and an Answer Bot (ABot) in the context of image-guessing.
Enhancing Visual Dialog State Tracking through Iterative Object-Entity Alignment in Multi-Round Conversations
MDST captures each round of dialog history, constructing internal dialogue state representations defined as 2-tuples of vision-language representations.
From Text to Insight: Leveraging Large Language Models for Performance Evaluation in Management
This study explores the potential of Large Language Models (LLMs), specifically GPT-4, to enhance objectivity in organizational task performance evaluations.
Generative AI Enhances Team Performance and Reduces Need for Traditional Teams
Recent advancements in generative artificial intelligence (AI) have transformed collaborative work processes, yet the impact on team performance remains underexplored.
Look into the Future: Deep Contextualized Sequential Recommendation
In LIFT, the context of a target user's interaction is represented based on i) his own past behaviors and ii) the past and future behaviors of the retrieved similar interactions from other users.
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs
Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably falls well behind advances in other domains such as computer vision or natural language processing.
Retrieval and Distill: A Temporal Data Shift-Free Paradigm for Online Recommendation System
Current recommendation systems are significantly affected by a serious issue of temporal data shift, which is the inconsistency between the distribution of historical data and that of online data.
Quantum-activated neural reservoirs on-chip open up large hardware security models for resilient authentication
Quantum artificial intelligence is a frontier of artificial intelligence research, pioneering quantum AI-powered circuits to address problems beyond the reach of deep learning with classical architectures.
AACP: Aesthetics assessment of children's paintings based on self-supervised learning
To solve this problem, we construct an aesthetics assessment dataset of children's paintings and a model based on self-supervised learning.
Content-Conditioned Generation of Stylized Free hand Sketches
We explore the performance of the model, including using styles randomly sampled from a prior normal distribution to generate images with various free-hand sketching styles, disentangling the painters' styles from known free-hand sketches to generate images with specific styles, and generating images of unknown classes that are not in the training set.
A multimodal gesture recognition dataset for desktop human-computer interaction
Gesture recognition is an indispensable component of natural and efficient human-computer interaction technology, particularly in desktop-level applications, where it can significantly enhance people's productivity.
Explicit Visual Prompts for Visual Object Tracking
Specifically, we utilize spatio-temporal tokens to propagate information between consecutive frames without focusing on updating templates.
HOIAnimator: Generating Text-prompt Human-object Animations using Novel Perceptive Diffusion Models
To date the quest to rapidly and effectively produce human-object interaction (HOI) animations directly from textual descriptions stands at the forefront of computer vision research.
Arbitrary Motion Style Transfer with Multi-condition Motion Latent Diffusion Model
Our MCM-LDM's cornerstone lies in its ability first to disentangle and then intricately weave together motion's tripartite components: motion trajectory motion content and motion style.
Mobility and Cost Aware Inference Accelerating Algorithm for Edge Intelligence
The model segmentation without user mobility has been investigated deeply by previous works.
High Efficiency Inference Accelerating Algorithm for NOMA-based Mobile Edge Computing
Specifically, when the mobile user has a large model inference task needed to be calculated in the NOMA-based MEC, it will take the energy consumption of both device and edge server and the inference latency into account to find the optimal model split strategy, subchannel allocation strategy (uplink and downlink), and transmission power allocation strategy (uplink and downlink).
Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution
Backprojection networks have achieved promising super-resolution performance for nature images but not well be explored in the remote sensing image super-resolution (RSISR) field due to the high computation costs.
Efficient Real-time Path Planning with Self-evolving Particle Swarm Optimization in Dynamic Scenarios
Particle Swarm Optimization (PSO) has demonstrated efficacy in addressing static path planning problems.
Hard Region Aware Network for Remote Sensing Change Detection
Finally, the hard region aware features extracted from the online hard region estimation branch and multi-level temporal difference features are aggregated into a unified feature representation to improve the accuracy of CD.
Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity
Meanwhile, the Sparrow simulator utilizes a 2D grid-based world, simplified kinematics, and conversion-free data flow to achieve a lightweight design.
Ranked #1 on
Atari Games
on Atari 2600 Pong
Corner Detection Based on Multi-directional Gabor Filters with Multi-scales
In this work, the capability of the Gabor function to discriminate the intensity changes of step edges, L-shaped corners, Y-shaped or T-shaped corners, X-shaped corners, and star-shaped corners are investigated.
UMIFormer: Mining the Correlations between Similar Tokens for Multi-View 3D Reconstruction
We empirically demonstrate on ShapeNet and confirm that our decoupled learning method is adaptable for unstructured multiple images.
Ranked #2 on
3D Object Reconstruction
on Data3D−R2N2
An Adaptive Plug-and-Play Network for Few-Shot Learning
Few-shot learning (FSL) requires a model to classify new samples after learning from only a few samples.
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Analog in-memory computing (AIMC) -- a promising approach for energy-efficient acceleration of deep learning workloads -- computes matrix-vector multiplications (MVMs) but only approximately, due to nonidealities that often are non-deterministic or nonlinear.
Short-length SSVEP data extension by a novel generative adversarial networks based framework
This study substantiates the feasibility of the proposed method to extend the data length for short-time SSVEP signals for developing a high-performance BCI system.
Multimodal Remote Sensing Image Registration Based on Adaptive Multi-scale PIIFD
Therefore, this paper proposes a multimodal remote sensing image registration method based on adaptive multi-scale PIIFD(AM-PIIFD).
GARNet: Global-Aware Multi-View 3D Reconstruction Network and the Cost-Performance Tradeoff
Deep learning technology has made great progress in multi-view 3D reconstruction tasks.
Lexical semantics enhanced neural word embeddings
To improve vector space models in deriving semantic similarity, we post-process neural word embeddings through deep metric learning, through which we can inject lexical-semantic relations, including syn/antonymy and hypo/hypernymy, into a distributional space.
Synonym Detection Using Syntactic Dependency And Neural Embeddings
By comparing various Vector Space Models in detecting synonyms in TOEFL, we systematically study the salience of syntactic dependencies in accounting for distributional similarity.
In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory
We demonstrate for the first time how the EM unit can physically superpose multiple training examples, expand to accommodate unseen classes, and perform similarity search during inference, using operations on an IMC core based on phase-change memory (PCM).
Constructing Trajectory and Predicting Estimated Time of Arrival for Long Distance Travelling Vessels: A Probability Density-based Scanning Approach
In this study, a probability density-based approach for constructing trajectories is proposed and validated through an typical use-case application: Estimated Time of Arrival (ETA) prediction given origin-destination pairs.
Optimizing Coordinative Schedules for Tanker Terminals: An Intelligent Large Spatial-Temporal Data-Driven Approach -- Part 1
In this study, a novel coordinative scheduling optimization approach is proposed to enhance port efficiency by reducing average wait time and turnaround time.
Optimizing Coordinative Schedules for Tanker Terminals: An Intelligent Large Spatial-Temporal Data-Driven Approach -- Part 2
In this study, a novel coordinative scheduling optimization approach is proposed to enhance port efficiency by reducing weighted average turnaround time.
AnomMAN: Detect Anomaly on Multi-view Attributed Networks
It remains a challenging task to jointly consider all different kinds of interactions and detect anomalous instances on multi-view attributed networks.
Real-time division-of-focal-plane polarization imaging system with progressive networks
Division-of-focal-plane (DoFP) polarization imaging technical recently has been applied in many fields.
Anomaly Detection via Self-organizing Map
Anomaly detection plays a key role in industrial manufacturing for product quality control.
Ranked #69 on
Anomaly Detection
on MVTec AD
A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection
In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases.
Multimodal Gait Recognition for Neurodegenerative Diseases
In recent years, single modality based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognised that each of the established approaches has different strengths and weaknesses.
SeqGenSQL -- A Robust Sequence Generation Model for Structured Query Language
We explore using T5 (Raffel et al. (2019)) to directly translate natural language questions into SQL statements.
SWIPENET: Object detection in noisy underwater images
Moreover, inspired by the human education process that drives the learning from easy to hard concepts, we here propose the CMA training paradigm that first trains a clean detector which is free from the influence of noisy data.
Bridging the gap between photovoltaics R&D and manufacturing with data-driven optimization
Novel photovoltaics, such as perovskites and perovskite-inspired materials, have shown great promise due to high efficiency and potentially low manufacturing cost.
Applied Physics
The Prevalence of Errors in Machine Learning Experiments
Objective: We investigate the incidence of errors in a sample of machine learning experiments in the domain of software defect prediction.
Deep Learning-based Radiomic Features for Improving Neoadjuvant Chemoradiation Response Prediction in Locally Advanced Rectal Cancer
The model built with handcrafted features achieved the mean area under the ROC curve (AUC) of 0. 64, while the one built with DL-based features yielded the mean AUC of 0. 73.
Deep Neural Net with Attention for Multi-channel Multi-touch Attribution
Customers are usually exposed to online digital advertisement channels, such as email marketing, display advertising, paid search engine marketing, along their way to purchase or subscribe products( aka.
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval
In addition, we harness a self-supervised semantic network to discover high-level semantic information in the form of multi-label annotations.
Detection and Attention: Diagnosing Pulmonary Lung Cancer from CT by Imitating Physicians
This paper proposes a novel and efficient method to build a Computer-Aided Diagnoses (CAD) system for lung nodule detection based on Computed Tomography (CT).
