3447 papers
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.
CogBlender is a novel framework that enables continuous, multi-dimensional control over the cognitive properties of text-to-image generation by mapping cognitive space to visual semantics and dynamically steering the flow-matching process through interpolated velocity fields guided by cognitive anchors.
The paper introduces MDTrack, a novel multimodal object tracking framework that achieves state-of-the-art performance by employing a Mixture of Experts for adaptive modality-aware fusion and utilizing decoupled State Space Models with cross-attention mechanisms for independent yet synergistic temporal propagation.
ToolRosetta is a unified framework that automatically transforms heterogeneous open-source code repositories into standardized, secure, and executable Model Context Protocol (MCP) tools, enabling LLM agents to autonomously plan and invoke specialized software for complex tasks with minimal human intervention.
The paper introduces See, Plan, Rewind (SPR), a progress-aware vision-language-action framework that enhances robotic manipulation robustness by dynamically grounding instructions into spatial subgoals and enabling closed-loop error recovery through state rewinding, achieving state-of-the-art performance on challenging benchmarks without additional training.
This study systematically investigates how network latency degrades the pragmatic and hedonic dimensions of Quality of Experience in networked VR whiteboards by analyzing their varying impacts across sequential and free collaboration modes, as well as between avatar-enhanced and traditional VR or PC-based systems.
CORAL is a scalable, embodiment-agnostic framework that mitigates multi-task interference and catastrophic forgetting in Vision-Language-Action models by freezing a shared backbone and dynamically routing language instructions to task-specific, lightweight LoRA experts with zero inference overhead.