2530 papers
This paper introduces Quantization-aware Distilled Restoration (QDR), a novel framework that employs Decoder-Free Distillation and Learnable Magnitude Reweighting to overcome bottlenecks in quantization-aware training, enabling high-performance, edge-deployed image restoration models that achieve near-FP32 accuracy and significant speedups.
This paper presents OTPL-VIO, a robust stereo visual-inertial odometry system that enhances performance in low-texture and illumination-challenging environments by employing a training-free deep descriptor with entropy-regularized optimal transport for line association and introducing adaptive uncertainty weighting to stabilize estimation.
This paper initiates the study of proportionality degree in participatory budgeting by establishing tight theoretical bounds for the Method of Equal Shares and Phragmen's Sequential Rule, demonstrating that despite their differing axiomatic properties, they achieve comparable quantitative proportionality, a finding further validated through extensive experiments on real-world datasets.
The paper presents DiffWind, a physics-informed differentiable framework that unifies wind-object interaction modeling, video-based reconstruction, and forward simulation by combining 3D Gaussian Splatting, the Material Point Method, and Lattice Boltzmann constraints to accurately recover and simulate wind-driven object dynamics from video observations.
VarSplat is an uncertainty-aware 3D Gaussian Splatting SLAM system that explicitly learns per-splat appearance variance to render differentiable uncertainty maps, thereby guiding tracking and optimization toward reliable regions to achieve robust pose estimation and high-fidelity reconstruction in challenging real-world environments.
This paper introduces FootMR, a method that refines 3D foot motion in markerless monocular human motion capture by lifting 2D keypoints to 3D using large-scale motion capture data and context-aware residual prediction, alongside the new MOOF dataset, to significantly outperform existing state-of-the-art approaches in accuracy and generalization.
This paper introduces DRIFT, a dual-path Transformer model that effectively fuses fine-grained local and coarse-grained global features from sparse 4D radar point clouds to achieve state-of-the-art performance in automated driving perception tasks like object detection and free road estimation.
The paper introduces TemporalDoRA, a parameter-efficient fine-tuning method that integrates lightweight temporal attention into the low-rank adaptation branch of vision encoders to enhance robustness against linguistic variations in surgical video question answering, validated by a new colonoscopy dataset and improved Out-of-Template performance.
This paper introduces the concept of "Interaction Smells" in multi-turn human-LLM code generation, establishes a taxonomy based on real-world data, analyzes their distribution across leading models, and proposes the Invariant-aware Constraint Evolution (InCE) framework to effectively mitigate these issues and improve task success rates.
The paper proposes TriFusion-SR, a wavelet-guided conditional diffusion framework that jointly performs tri-modal medical image fusion and super-resolution by decomposing features into frequency bands and employing rectified wavelet features with adaptive spatial-frequency fusion to achieve state-of-the-art performance in resolution and perceptual quality.
ProGS introduces a novel streaming-friendly codec that organizes 3D Gaussian Splatting data into an octree structure with mutual information enhancement, achieving a 45-fold reduction in file size and over 10% visual improvement while enabling progressive coding for varying bandwidth conditions.
This paper introduces Robotic Scene Cloning (RSC), a novel method that enhances zero-shot robotic manipulation by editing existing operation trajectories through visual prompting and condition injection to generate accurate, scene-consistent samples that significantly improve policy generalization in real-world environments.
This paper introduces GSStream, a novel volumetric scene streaming system for 3D Gaussian Splatting that leverages collaborative viewport prediction and deep reinforcement learning-based bitrate adaptation to overcome bandwidth challenges and deliver high-quality, real-time immersive experiences.
The paper proposes FrameDiT, a novel video generation architecture that introduces Matrix Attention to efficiently model global spatio-temporal dynamics by processing frames as matrices, thereby achieving state-of-the-art video quality and temporal coherence while maintaining computational efficiency comparable to local factorized attention.
This paper introduces the concept of "local stability" to assess how minor changes in item values affect their rankings, particularly within dense regions of similar-quality items, and proposes efficient sampling-based algorithms (LStability and Detect-Dense-Region) with theoretical guarantees to compute this measure and identify such regions.
This paper formalizes the concept of idempotent backward slices and presents a sound, efficient algorithm for extracting them from Gated Static Single Assignment (GSA) form to enable a novel sparse code-size reduction optimization that merges non-contiguous instructions, achieving up to 7.24% size reduction in specific benchmarks.
This paper proposes a regularized ensemble Kalman filter framework that integrates sensor displacement data into stochastic phase-field models of brittle fracture to infer the evolving displacement and phase-field states, thereby correcting model predictions while ensuring physical consistency through a novel regularization step.
The paper introduces WVA, a global optimization control plane co-designed with the \texttt{llmd} inference engine that leverages internal saturation states and fragmentation-aware strategies to achieve significantly higher throughput, fewer request failures, and lower power consumption compared to traditional Kubernetes autoscalers when managing heterogeneous LLM workloads.
FetalAgents is a novel multi-agent system that dynamically orchestrates specialized vision experts to deliver robust, end-to-end fetal ultrasound analysis and structured clinical reporting across multiple tasks, outperforming existing specialized models and multimodal large language models.
This paper proposes a task-based scheduling framework that ensures end-to-end data freshness in safety-critical multi-rate systems by introducing a Consensus Offset Search algorithm to align task releases with data lifespan constraints, thereby eliminating the artificial latency of Logical Execution Time and the inefficiency of redundant oversampling while preserving Global EDF schedulability.