5037 papers
LD-RPS proposes a novel, dataset-free, and unified image restoration framework that leverages recurrent posterior sampling on a pretrained latent diffusion model, enhanced by multimodal semantic priors and a lightweight alignment module, to achieve superior performance across various degradation types without task-specific training.
This paper establishes that Physics-Informed Neural Networks (PINNs) require a network size scaling with the number of noisy samples to achieve empirical risk below the noise variance, demonstrating that simply increasing data quantity cannot compensate for insufficient model capacity in noisy PDE training.
This paper proposes MCULoRA, a novel parameter-efficient framework featuring modality combination aware low-rank adaptation and dynamic parameter fine-tuning to resolve gradient conflicts and improve performance in incomplete multimodal emotion recognition.
This paper identifies a pervasive "agreement bias" in Multimodal LLM verifiers that causes them to over-validate agent behavior, and proposes a lightweight Self-Grounded Verification (SGV) method that significantly improves failure detection and task completion across web navigation, computer use, and robotics by decoupling prior generation from trajectory evaluation.
The paper proposes Mamba Snake, a novel deep snake framework enhanced by state space modeling and a dual-classification synergy mechanism, which effectively addresses the challenges of multi-scale structural heterogeneity in Unified Medical Image Segmentation by modeling inter-organ topological relationships and refining complex morphologies to achieve superior performance across five clinical datasets.
This paper introduces InsightX Agent, a novel Large Multimodal Model-based agentic framework that integrates a Sparse Deformable Multi-Scale Detector with an Evidence-Grounded Reflection tool to achieve reliable, interpretable, and interactive X-ray non-destructive testing analysis, demonstrated by a 96.54% F1-score on the GDXray+ dataset.
This paper proposes a Vision Transformer-based framework that leverages PCA-driven weak supervision to expand limited manual annotations for refining disaster-affected area segmentation using Sentinel-2 and Formosat-5 imagery, thereby enhancing the reliability and scalability of the Taiwan Space Agency's Emergent Value Added Product (EVAP) in scenarios with scarce ground truth.
This paper presents the first comprehensive survey of flow matching applications in biology and life sciences, systematically reviewing its theoretical foundations and categorizing its recent advancements in biological sequence modeling, molecule design, and protein generation.
This paper introduces User Goal State Tracking (UGST), a novel framework and three-stage methodology that enables LLM-based user simulators to autonomously track goal progression and generate goal-aligned responses, significantly improving performance on MultiWOZ 2.4 and -Bench benchmarks.
The paper introduces CauKer, a novel algorithm that combines Gaussian Process kernel composition with Structural Causal Models to generate diverse, causally coherent synthetic time series, enabling sample-efficient pre-training of classification foundation models that exhibit clear scaling laws across varying dataset sizes and model capacities.
GraphProp is a two-phase framework for training graph foundation models that first learns structural generalization by predicting graph invariants and then leverages these representations as positional encodings to enhance cross-domain performance in graph-level tasks, particularly outperforming existing methods in scenarios with limited data or missing node attributes.
Video-EM introduces a training-free, event-centric episodic memory framework that enhances long-form video understanding by orchestrating an LLM to localize, segment, and refine query-relevant moments into a compact, temporally coherent event timeline, thereby overcoming the context limitations of existing Video-LLMs without requiring architectural changes.
UniCast is a parameter-efficient framework that enhances Time Series Foundation Models through instance-conditioned prompting and dynamic modality routing, enabling effective adaptation to multimodal inputs and instance-level variations without updating the frozen forecasting backbone.
The paper introduces ECHO, a novel foundation model that leverages band-split architecture and frequency positional embeddings to achieve state-of-the-art performance in anomaly detection and fault classification across variable-length, arbitrary sampling rate machine signals without requiring padding or cropping.
This paper proposes a unified training framework that combines entropy-driven curriculum learning, which sequences training from simple to complex trajectories based on Lempel-Ziv compression, with multi-task learning to simultaneously optimize location, distance, and direction predictions, thereby achieving state-of-the-art performance and significantly faster convergence in human mobility prediction.
This paper proposes a hybrid control framework that enhances quadrotor resilience in underground environments by using a normalizing flow-based prior as a runtime monitor to dynamically switch between a learning-based controller for efficiency and a safety controller for collision avoidance when encountering out-of-distribution scenarios.
The paper proposes OTESGN, a novel aspect-based sentiment analysis model that integrates syntactic graph attention with semantic optimal transport to effectively capture nonlinear associations and suppress noise, achieving state-of-the-art performance on multiple benchmark datasets.
This study presents a novel computer vision framework that utilizes external observation techniques, including YOLO-based object detection and lane monitoring, to classify distracted and impaired driver behaviors in non-connected vehicles without relying on inter-vehicular communication.
This paper introduces a modular, multimodal framework that leverages generative AI to synthesize realistic residential building data from public images and information, thereby overcoming data accessibility and privacy barriers to advance energy modeling and machine learning research.
This paper introduces MICA, a privacy-preserving, speech-interactive multi-agent system that leverages Adaptive Step Fusion and a safety-audited coordination topology to deliver robust, real-time industrial assistance for assembly and maintenance tasks on resource-constrained hardware.