5139 papers
SAGE is a zero-shot, structure-aware generative framework that synthesizes visually coherent and motion-consistent video transitions between diverse clips by combining line maps and motion flow guidance with generative synthesis, effectively outperforming existing classical and generative methods without requiring fine-tuning or specific training data.
This paper presents MobiDock, a modular self-reconfigurable bimanual mobile manipulator that utilizes an autonomous computer vision-based docking mechanism to physically unite two independent robots, thereby transforming complex multi-robot coordination into a simpler single-system control problem that significantly improves dynamic stability, precision, and operational efficiency.
This paper introduces VOPP, a novel vectorized online POMDP planner that eliminates synchronization bottlenecks by representing all planning data as tensors and performing fully parallelized expectation estimations, achieving a 20-fold efficiency gain over existing parallel solvers and outperforming state-of-the-art sequential methods with a 1000-fold reduction in planning budget.
This paper proposes a forensic method called "diffusion snap-back reconstruction," which detects AI-generated images by analyzing how perceptual similarity metrics change when an image is perturbed and reconstructed by a diffusion model, achieving high accuracy (AUROC of 0.993) and robustness against common distortions without relying on traditional pixel-level artifacts.
This paper introduces PhantomFetch, a hardware-agnostic defense that secures IP-stride prefetchers against side-channel attacks by obfuscating sensitive load effects to break exploitable couplings, thereby maintaining prefetching performance without requiring hardware modifications.
This paper introduces MUGSQA, a novel framework comprising a multi-uncertainty-based Gaussian Splatting quality assessment dataset, a unified multi-distance subjective evaluation method, and two benchmarks designed to rigorously assess the robustness of reconstruction methods and the performance of existing quality metrics under varying input conditions.
This paper introduces CountOCC, a novel amodal counting framework that overcomes the limitations of existing methods under occlusion by hierarchically reconstructing complete object features through multimodal guidance and visual equivalence objectives, achieving state-of-the-art performance on newly established occlusion-augmented benchmarks.
The paper proposes LAMP, a language-augmented multi-agent reinforcement learning framework that employs a "Think-Speak-Decide" pipeline to integrate unstructured language with numerical data, significantly outperforming existing baselines in economic decision-making through improved cumulative returns, robustness, and interpretability.
The paper proposes Video2Layout, a two-stage framework that reconstructs metric-grounded spatial layouts using continuous object boundary coordinates instead of discretized grids, thereby enhancing fine-grained spatial reasoning in Multimodal Large Language Models and achieving superior performance on spatial benchmarks.
This paper proposes the Multi-Order Matching Network (MOMNet), an alignment-free framework that achieves state-of-the-art depth super-resolution by adaptively retrieving and integrating misaligned RGB information through a novel multi-order matching and aggregation mechanism.
This paper introduces DualMindVLM, a visual language model that leverages a dual-mode thinking mechanism to dynamically select between fast, intuitive responses and slow, deliberate reasoning based on problem complexity, thereby achieving state-of-the-art performance with significantly improved token efficiency.
The paper proposes the Radiative-Structured Neural Operator (RSNO), a novel deep learning framework that reconstructs hyperspectral images from multispectral observations by learning continuous spectral mappings under radiative priors and employing angular-consistent projection to ensure physical consistency and eliminate color distortion.
The paper proposes UnfoldLDM, a deep unfolding framework that integrates a multi-granularity degradation-aware module for robust degradation estimation and a degradation-resistant latent diffusion model with an over-smoothing correction transformer to effectively address blind image restoration by overcoming degradation-specific dependencies and suppressing over-smoothing bias.
This study analyzes Reddit discourse to reveal how users collectively negotiate ChatGPT privacy risks through practices of risk signaling, norm-setting, and advocacy for privacy-preserving alternatives, offering insights for AI design and privacy literacy.
This paper presents a stable, real-time multi-drone GNSS tracking system for marine robots that integrates visual detection, multi-object tracking, triangulation, and a confidence-weighted Extended Kalman Filter with cross-drone ID alignment to overcome the limitations of underwater signal loss and traditional alternatives.
This paper introduces Yo'City, an agentic framework that leverages large models for hierarchical planning and a self-critic expansion loop to generate personalized, boundless, and spatially coherent 3D realistic city scenes, outperforming existing state-of-the-art methods across multiple evaluation metrics.
DOPD is a dynamic LLM inference system that optimizes goodput and meets strict SLOs by adaptively adjusting the ratio of prefill to decoding instances based on real-time workload monitoring, outperforming existing aggregation and disaggregation approaches like vLLM and DistServe.
This paper initiates the study of fault-tolerant spanners in hypergraphs by demonstrating that simple extensions fail to achieve sublinear size, and subsequently proposes a novel clustering-based algorithm that constructs edge fault-tolerant hyperspanners with improved sublinear size bounds alongside a matching lower bound.
This paper presents an LLM-assisted multi-agent framework that automates the design, tuning, and adaptation of control systems for roll-to-roll manufacturing, significantly reducing manual effort while ensuring safety and maintaining high-quality tension and velocity regulation under model uncertainty.
This paper introduces RadDiff, a novel retrieval-augmented denoising diffusion model that effectively integrates up-to-date protein knowledge into the inverse folding process, significantly outperforming existing methods in sequence recovery rate and foldability while offering greater parameter efficiency and scalability.