6583 papers
ExpReS-VLA is a specialized Vision-Language-Action model that enables rapid, memory-efficient on-device adaptation to specific robotic tasks by combining compressed experience replay, retrieval-augmented generation, and a novel contrastive loss to prevent catastrophic forgetting while significantly improving performance on both spatial and long-horizon benchmarks.
The paper introduces CAVER, a novel robot equipped with a specialized audio-exciting end-effector, a combined audiovisual representation, and a curiosity-driven exploration algorithm that efficiently learns object correlations to improve material classification and audio-only imitation tasks.
This paper proposes a dual-mechanism collaborative optimization framework that synergistically integrates structural fairness decoupling and global distribution alignment to enhance both inter-group and intra-group fairness in deepfake detection without compromising overall accuracy.
This habilitation thesis investigates polynomial algorithmics under space constraints, presenting time- and space-efficient algorithms for fundamental operations and introducing the first quasi-linear algorithm for sparse polynomial interpolation while addressing hard problems in sparse factorization and divisibility.
LaxMotion is a novel framework for 3D human motion generation that replaces precise 3D coordinate supervision with a relaxed paradigm based on global trajectories and monocular 2D cues, thereby enhancing model generalization and diversity while achieving performance comparable to fully supervised methods.
This paper presents a Coq-based formalization that extends Tarski's mereogeometry into a full topological space by proving the correspondence between mereological classes and regular open sets, thereby reducing Tarski's axiomatic system and enhancing its expressiveness for Euclidean geometric reasoning.
This paper introduces SPARK, a jailbreak framework that exploits cross-modal associations in text-to-video models by combining neutral scene anchors, latent auditory triggers, and stylistic modulators to generate semantically unsafe videos that bypass safety guardrails while maintaining a benign appearance.
The paper introduces MRIQT, a physics-aware 3D conditional diffusion model that significantly enhances the image quality and diagnostic fidelity of portable ultra-low-field neonatal MRI by translating noisy scans into high-fidelity images comparable to high-field MRI.
This paper proposes a contact-safe reinforcement learning framework that combines Proximal Policy Optimization with movement primitives and an energy-aware Cartesian impedance controller to generate robust, safe, and energy-efficient task-space trajectories for complex contact-rich manipulation in 3D environments.
This paper introduces WNumMPC, a hierarchical multi-agent navigation framework that combines a reinforcement learning-based planner and a model-based controller to resolve symmetry-induced deadlocks in dense environments by leveraging topological winding numbers for robust, communication-free coordination.
The paper introduces FunnyNodules, a fully parameterized synthetic dataset of lung nodule-like shapes with controllable visual attributes and known decision rules, designed to systematically evaluate and benchmark explainable AI models by verifying whether they learn correct attribute-target relations and align their attention with relevant diagnostic features.
Bi-AQUA is a novel bilateral control-based imitation learning framework for underwater robot arms that integrates transformer-based action chunking with explicit lighting modeling to achieve robust performance in challenging, variable illumination conditions.
This paper introduces the Unified Attention-Mamba (UAM) backbone, a flexible architecture that seamlessly integrates Attention and Mamba modules without manual tuning, achieving state-of-the-art performance in both tumor cell classification and image segmentation tasks on public benchmarks.
EchoVLA is a memory-enhanced Vision-Language-Action model for mobile manipulation that synergizes scene and episodic declarative memories to improve navigation and task performance, validated by the new MoMani benchmark and demonstrating significant gains over existing baselines in both simulation and real-world settings.
SyncMV4D is a novel framework that overcomes the limitations of single-view and data-hungry 3D methods by introducing a Multi-view Joint Diffusion model and a Diffusion Points Aligner to simultaneously generate synchronized, realistic multi-view hand-object interaction videos and globally aligned 4D metric motions through a closed-loop coupling of visual appearance and dynamic geometry.
This paper proposes a novel autonomous lane-changing planning framework that integrates dynamic risk fields with time-varying convex feasible spaces and a constrained iLQR solver to achieve safe, efficient, and comfortable trajectories that outperform traditional methods in complex traffic scenarios.
This paper introduces ReInversion, a training-free exemplar-guided image editing method that employs a two-stage reversible denoising process and a Mask-Guided Selective Denoising strategy to achieve state-of-the-art performance with minimal computational overhead.
This paper presents a sex-stratified whole-body MR image registration method for the UK Biobank that leverages subcutaneous adipose tissue and muscle masks to significantly outperform existing intensity-based and deep learning approaches in anatomical alignment and correlation analysis accuracy.
This paper introduces UniTS, a unified spatio-temporal generative model based on flow matching and diffusion transformers that integrates tasks like cloud removal, change detection, and forecasting into a single framework, significantly outperforming specialized models under challenging conditions.
This paper introduces Safe Model Predictive Diffusion (Safe MPD), a training-free planning framework that integrates a safety shield directly into the diffusion denoising process to generate kinodynamically feasible and safe trajectories in real-time, outperforming existing methods in success rate and safety without requiring post-processing corrections.