5561 papers
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.
The paper proposes UHDPromer, a neural discrimination-prompted Transformer that leverages Neural Discrimination Priors to measure feature differences and guide attention and gating mechanisms, achieving state-of-the-art efficiency and performance across three Ultra-High-Definition image restoration tasks.
PPC-MT is a novel parallel point cloud completion framework that combines a PCA-guided parallel synthesis strategy with a hybrid Mamba-Transformer architecture to achieve superior reconstruction quality and computational efficiency compared to state-of-the-art methods.
This paper introduces Multi-Membership Temporal Attention (MMTA), a unified single-stage transformer architecture that enhances fine-grained stroke rehabilitation assessment by enabling frames to attend to multiple temporal contexts simultaneously, thereby improving boundary sensitivity and performance on both video and IMU data without increasing model depth.
This paper proposes SMART, a novel semi-supervised framework leveraging a SAM3-based teacher-student architecture with motion-aware consistency and progressive confidence regularization to achieve state-of-the-art coronary artery segmentation in X-ray angiography videos while significantly reducing the need for annotated data.
This paper presents a novel blind nonlinear inverse framework that unifies anatomical image recovery, high-resolution cine synthesis, and 3D motion estimation for tagged MRI by synergistically combining MR physics with deep generative priors to overcome challenges like tag fading and imaging blur.
VEMamba is an efficient framework for isotropic reconstruction of Volume Electron Microscopy that leverages a novel 3D dependency reordering paradigm with Axial-Lateral Chunking Selective Scan and Dynamic Weights Aggregation modules, alongside MoCo-based degradation-aware training, to achieve high-performance, consistent 3D tissue imaging with a low computational footprint.
The paper introduces pySpatial, a zero-shot visual programming framework that enhances Multi-modal Large Language Models' spatial reasoning capabilities by generating Python code to interface with 3D spatial tools, thereby outperforming existing baselines on benchmarks and enabling successful real-world indoor navigation without fine-tuning.