2528 papers
The paper introduces TRIP-Bag, a portable, suitcase-contained teleoperation system that enables rapid, high-fidelity data collection for robot learning by eliminating the embodiment gap through direct joint-to-joint control in diverse, non-laboratory environments.
This paper introduces Flash-KMeans, an IO-aware and contention-free GPU implementation that eliminates memory bottlenecks in the assignment stage and resolves atomic write contention in the update stage through novel kernel-level innovations, achieving up to 17.9 speedup over existing baselines and enabling -means as a high-performance online primitive.
HelixTrack is a fully event-driven method that jointly tracks propeller-like objects and estimates their RPM with microsecond latency by back-warping events to a rotor plane and refining pose through phase-coupled geometry, validated on a newly introduced TQE dataset where it outperforms existing baselines.
This paper introduces MORLAX, a GPU-native multi-objective reinforcement learning algorithm, and MO-Playground, a suite of GPU-accelerated environments, which together enable massively parallelized training that achieves 25–270x speedups and superior Pareto fronts for complex robotics tasks compared to legacy CPU-based approaches.
The paper proposes RAE-NWM, a navigation world model that operates in a dense DINOv2 feature space using a Conditional Diffusion Transformer with a decoupled head and time-driven gating to achieve superior structural stability and action accuracy compared to traditional latent-space approaches.
This paper introduces Geometric Semantic Decoupling (GSD), a parameter-free module that enhances the generalizability of AI-generated image detectors by explicitly removing dominant semantic priors from learned representations, thereby forcing models to rely on robust forensic artifacts rather than failing via "semantic fallback" when encountering unseen generation pipelines.
This paper introduces Poly-DETR, a lightweight instance segmentation framework that reformulates the task as sparse vertex regression using polar representation and specialized attention mechanisms, achieving superior performance and reduced memory consumption compared to traditional mask-based methods, particularly in high-resolution and domain-specific scenarios.
This paper introduces "Reasoning-Oriented Programming," an automated attack framework that bypasses Large Vision-Language Model safety alignments by chaining semantically orthogonal benign visual inputs to force the emergence of harmful logic only during late-stage reasoning, thereby outperforming existing jailbreak methods on state-of-the-art models.
This paper introduces RF-Mem, a novel memory retrieval framework that mimics human dual-process cognition by adaptively switching between fast familiarity-based recognition and iterative recollection-based reconstruction to achieve scalable and effective personalization in large language models.
This paper introduces Platooning as a Service (PlaaS), a Stackelberg game-based decision-support framework that optimizes pricing and travel distance between service providers and users to enhance sustainable transportation, while analyzing how factors like government subsidies and vehicle velocity impact profitability and carbon emissions.
This paper proposes DiP, a novel multimodal graph representation learning framework that utilizes modality-specific pseudo nodes and dynamic information pathways to achieve adaptive, sparse, and linear-complexity message propagation, consistently outperforming existing baselines in link prediction and node classification tasks.
This paper introduces a large-scale framework for Vision-and-Language Navigation that leverages web-based room tour videos and implicit geometry representations to overcome simulator limitations, enabling robust zero-shot navigation agents with state-of-the-art performance across multiple benchmarks.
This paper investigates how end-to-end latency impacts interaction fluency and social presence in VR conferencing compared to traditional video conferencing through subjective experiments, aiming to clarify the underlying perceptual and cognitive mechanisms to guide system optimization.
This paper investigates the online weighted non-crossing matching problem in the Euclidean plane, establishing that while deterministic algorithms fail to achieve non-trivial competitive ratios, randomization enables constant competitiveness, and further analyzing variants involving revocability, collinear points, and advice complexity.
This paper proposes the Task-Aware Perceptual Interaction Fluency Model (TPIFM), which evaluates the perceived responsiveness of remote collaborative augmented reality by integrating network impairments with task-specific just-noticeable differences derived from the Free Energy Principle to guide adaptive system design.
ForgeDreamer is a novel text-to-3D generation framework designed for industrial applications that overcomes domain adaptation and geometric reasoning limitations by integrating a Multi-Expert LoRA Ensemble for interference-free cross-category generalization and a Cross-View Hypergraph approach for capturing high-order structural dependencies to ensure manufacturing-level precision.
The paper presents *FungiSync*, a multi-person mixed reality experience that translates the symbiotic interdependence of mycorrhizal networks into an embodied ritual where participants' individual digital perceptual worlds entangle through physical touch, fostering a "fungal epistemic" perspective that critiques accelerated individualism.
This paper proposes novel training strategies and losses, including Gaussian scale resetting and an entropy constraint on alpha blending, to shorten the Gaussian lists used in 3D Gaussian splatting, thereby significantly accelerating the learning process without compromising rendering quality.
The paper introduces Stable Video Object Removal (SVOR), a robust framework that achieves state-of-the-art, flicker-free video object removal under real-world imperfections by employing a Mask Union strategy for stable erasure, a Denoising-Aware Segmentation head for precise localization, and a Curriculum Two-Stage training approach to handle shadows, abrupt motion, and defective masks.
This paper introduces a novel, self-supervised feature field learning approach that enables the first feed-forward model for open-world 3D convex decomposition, producing high-quality, generalizable results across diverse representations like meshes, CAD models, and Gaussian splats to accelerate applications such as collision detection.