2579 papers
This paper proposes Dimensional Rational Multi-Agent Communication (DRMAC), a novel framework that enhances multi-agent cooperation by applying dimensional analysis to mitigate redundancy and confounders in message embeddings through redundancy-reduction regularization and dynamic gradient masking.
The paper proposes SSRNet, a robust facial expression recognition framework that combines a structure-prior-guided feature enhancement module with self-supervised contrastive learning to effectively address real-world challenges like noise and occlusion, achieving state-of-the-art performance on FER2013 and RAF-DB datasets.
This paper introduces RefQA, a million-scale dataset of 1.52 million cross-document clinical citation-evidence question-answer records derived from PubMed Central, featuring structured metadata, verifiable claim grounding, and high-quality annotations to support research in clinical citation analysis.
This paper proposes ER-Curriculum-DDQN, a deep reinforcement learning framework that integrates feasibility masking, critical task pressure features, and reservation-guided curriculum learning to optimize online task offloading in UAV-MEC systems via transparent LEO relays, thereby maximizing the timely completion ratio of critical tasks under strict service window constraints.
This paper proposes the RYAS-LOFCN framework, which integrates a Residual Atrous Squeeze Attention Network with FPN and a YOLOv12-L based LightGBM-optimized Fuzzy CatBoost network to overcome limitations in detecting distant and visually similar traffic objects, achieving 98.63% accuracy and 98.56% precision in vehicle identification.
This paper proposes a deployment-aware framework for selecting learned RGB-D codecs in edge-cloud 3D reconstruction systems, demonstrating that prioritizing explicit hardware and runtime constraints over offline rate-distortion performance yields a practical solution that balances encoding speed, memory usage, and reconstruction quality better than traditional hardware baselines.
This paper proposes a hybrid semantic–lexical framework that integrates ontology-based reasoning with NLP-driven lexical validation to significantly enhance contextual anomaly detection and intelligent data correction in heterogeneous textual datasets, as demonstrated by superior performance on polluted healthcare data compared to traditional lexical-only approaches.
The paper proposes Agent-MIRUPD, an LLM-based agent framework that unifies observability data with structured multi-step reasoning to automate the full microservices incident-response lifecycle, achieving significant improvements in diagnosis speed, mitigation accuracy, and token efficiency compared to existing baselines.
This paper demonstrates that while exploiting temporal coherence to incrementally maintain spatial hash tables can significantly accelerate particle neighbor searches in coherent motion scenarios, its performance advantage is highly sensitive to particle movement and table load, often making full reconstruction the safer choice when these factors exceed specific thresholds.
DFL-YOLO is a novel detection framework that integrates Large Selective Kernels, a Density-Guided Frequency Decoupling Branch, and a Multi-Strategy Weighted IoU loss to effectively address scale variation, feature ambiguity, and localization errors in dense multi-class tree crown detection from UAV imagery.