8681 papers
This paper proposes a novel k-space dual channel U-Net framework that directly reconstructs high-quality, high-field-like MRI images from undersampled low-field k-space data, outperforming traditional spatial-domain methods and achieving quality comparable to full k-space acquisitions.
This paper introduces the Mixture of Low-Rank Experts (MoLRE) framework, a parameter-efficient fine-tuning method that significantly enhances the performance of diverse foundation models on comprehensive multi-label head CT diagnosis by employing specialized low-rank adapters and unsupervised soft routing without requiring explicit pathology supervision.
The paper proposes CoLC, a communication-efficient collaborative perception framework that leverages LiDAR completion techniques—specifically Foreground-Aware Point Sampling, Completion-Enhanced Early Fusion, and Dense-Guided Dual Alignment—to restore scene completeness from sparse transmissions and achieve superior perception-communication trade-offs while remaining robust to model heterogeneity.
SCOUT is a fast, self-supervised spectral CT reconstruction method that leverages spatial nonlocal similarity and projection domain conjugate properties to generate pseudo-3D data, enabling high-fidelity imaging with detail recovery and artifact mitigation under ultra-low data regimes without requiring external datasets or pre-training.
This paper proposes STMI, a novel multi-modal object Re-Identification framework that integrates segmentation-guided feature modulation, semantic token reallocation, and cross-modal hypergraph interaction to enhance foreground representation, preserve discriminative cues, and capture high-order semantic relationships while mitigating background noise.
TokenSplat is a feed-forward framework that achieves joint 3D Gaussian reconstruction and camera pose estimation from unposed multi-view images by introducing a token-aligned prediction module and an asymmetric dual-flow decoder to enable robust, iterative-free 3D scene modeling.
This paper proposes a Universal Khmer Text Recognition (UKTR) framework featuring a novel modality-aware adaptive feature selection (MAFS) technique to overcome data scarcity and modality-specific limitations, achieving state-of-the-art performance while introducing the first comprehensive benchmark for the task.
This paper addresses the scarcity of annotated data for Khmer scene document layout analysis by introducing a comprehensive framework that includes a new benchmark dataset, an open-source augmentation tool for synthetic data generation, and YOLO-based models with oriented bounding boxes to handle the script's structural complexities and geometric distortions.
This paper introduces IU, a novel imperceptible universal backdoor attack that leverages graph convolutional networks to generate stealthy, class-specific perturbations, achieving high attack success rates with minimal poisoning while evading existing defenses.
This paper presents an industrial pipeline inner wall reconstruction system that utilizes panoramic image stitching and polar coordinate transformation on endoscopic video to generate comprehensive planar panoramic images, thereby significantly improving the efficiency and accuracy of defect detection compared to traditional frame-by-frame review methods.
This paper proposes an anti-feature-collapse learning framework that preserves diverse and complementary forgery cues in the representation space to overcome the generalization limitations of existing methods, significantly improving cross-model detection accuracy and robustness against unseen generative mechanisms.
UniHM introduces a unified framework for dexterous hand manipulation that leverages a shared tokenizer for diverse hand morphologies and a vision-language action model trained on human-object interactions to generate physically feasible, human-like manipulation sequences from open-vocabulary language instructions without requiring extensive real-world teleoperation data.
This paper presents a self-supervised spatiotemporal diffusion autoencoder that leverages longitudinal CT images and time-from-onset data to achieve state-of-the-art prediction of stroke severity and functional outcomes with minimal labeled data.
This paper bridges the gap in training-free sampling acceleration for text-to-image diffusion models by systematically analyzing their design space and proposing the Constant Total Rotation Schedule (TORS), a geometrically inspired strategy that achieves high-quality image generation in just 10 sampling steps across various models.
This paper introduces DUCX, a systematic audit framework that decomposes demographic bias in tool-using chest X-ray agents into tool exposure, transition, and reasoning components, revealing that significant subgroup disparities exist in intermediate agentic behaviors that are often overlooked by end-to-end performance evaluations.
This paper introduces the Neural Functional Alignment Space (NFAS), a brain-referenced framework that characterizes diverse artificial neural networks by modeling their layer-wise dynamics via Dynamic Mode Decomposition and projecting them into a biologically anchored coordinate system to reveal structured modality-specific clustering and cross-modal convergence.
This paper introduces ConVOLT, a conformal prediction framework that enhances uncertainty quantification for template-based medical image segmentation by calibrating volumetric intervals based on deformation field properties, thereby achieving target coverage with significantly tighter bounds than existing output-space methods.
NERFIFY is a multi-agent framework that automates the conversion of Neural Radiance Field research papers into runnable Nerfstudio plugins by leveraging domain-specific constraints, dependency-aware synthesis, and visual feedback to achieve expert-level code quality in minutes rather than weeks.
The paper introduces COMBAT, a real-time diffusion-based world model trained on Tekken 3 that leverages causal distillation and diffusion forcing to generate sophisticated, reactive opponent behaviors from single-player data without requiring explicit policy supervision.
This paper introduces MME, a novel Mixture of Experts framework for mesh analysis that utilizes a Random Walk Transformer gating mechanism and dynamic loss balancing to effectively combine diverse expert models, achieving state-of-the-art performance in mesh classification, retrieval, and semantic segmentation.