3499 papers
This paper introduces MixMFL, a novel non-centralized federated learning paradigm for MRI image segmentation that addresses client-wise modality and data heterogeneity through a proposed MDM-MixMFL framework featuring modality decoupling for tailored and shared updates, and a memorizing mechanism to compensate for incomplete local modalities.
UltraUPConvNet is a computationally efficient, multi-task framework based on UPerNet and ConvNeXt that simultaneously performs ultrasound tissue segmentation and disease prediction, achieving state-of-the-art performance on a large-scale dataset with reduced computational overhead.
Traffic-MLLM is a retrieval-free neural framework that enhances autonomous driving decision-making by integrating multi-source traffic data with a curiosity-driven refinement mechanism to learn a structured, generalizable case space for robust reasoning in long-tail scenarios.
ActivePose proposes an active 6D object pose estimation and tracking framework that integrates a Vision-Language Model with "robotic imagination" to dynamically resolve viewpoint-induced ambiguities through Next-Best-View selection and employs a diffusion-policy for robust camera trajectory control, significantly outperforming classical baselines in both simulation and real-world robotic manipulation tasks.
Inspired by mudskippers, this study demonstrates that actively oscillating a robot's tail significantly enhances crawling speed and reduces drag on deformable granular terrains by fluidizing the substrate, offering new design principles for locomotion in challenging environments.
The paper proposes SAGA, a novel linear attention mechanism that employs selective adaptive gating and efficient Hadamard-product decomposition to overcome the low-rank limitations of existing methods, thereby achieving significant gains in computational efficiency, memory usage, and top-1 accuracy on high-resolution vision tasks.
This paper introduces the Cumulative Consensus Score (CCS), a label-free and model-agnostic metric that evaluates object detector reliability in deployment by measuring the spatial consistency of predictions across test-time augmented views, achieving high congruence with traditional ground-truth-based metrics.
This paper introduces WHU-STree, a comprehensive, multi-modal benchmark dataset featuring synchronized point clouds and high-resolution images of over 21,000 street trees across two cities, designed to overcome limitations in existing datasets by enabling diverse inventory tasks and advancing research in multi-modal fusion and cross-domain generalization for urban tree management.
This paper proposes an asynchronous reinforcement learning framework equipped with a Temporal Encoding Module and a two-stage curriculum to decouple high-frequency control from low-frequency perception, enabling robust, agile autonomous aerial navigation in complex real-world environments with zero-shot sim-to-real transfer.
GeoAware-VLA enhances the viewpoint generalization of Vision-Language-Action models by integrating features from a frozen, pretrained geometric vision model via a lightweight projection layer, achieving significant improvements in zero-shot performance on unseen camera poses across both simulation benchmarks and real-world robotic platforms without requiring explicit 3D training data.
This paper introduces the Object-Adaptive Impact Point Predictor (OIPP) and a new real-world dataset of 8,000 diverse trajectories to enable quadruped robots with baskets to accurately predict the landing positions of various in-flight objects, even during early flight stages and for unseen objects, thereby significantly improving catching success rates.
This paper introduces LibriTTS-VI, the first public corpus for numerical voice impression control, and proposes novel training methods to mitigate impression leakage, achieving significantly improved controllability over existing prompt-based approaches.
This paper introduces a scene graph-based framework that enhances the compositional generalization of generalist robots by learning robust, focused atomic skills via graph neural networks and diffusion models, which are then orchestrated by a vision-language model planner to achieve superior performance in complex, long-horizon tasks.
DroFiT is a lightweight, single-microphone speech enhancement network that integrates a frequency-wise Transformer with a hybrid encoder-decoder and TCN back-end to achieve real-time, memory-efficient drone noise removal on resource-constrained UAV platforms.
This paper presents a novel event-camera-based visual teach-and-repeat system that achieves ultra-low latency (2.88 ms) and robust autonomous navigation over 3000+ meters in diverse conditions by utilizing fast Fourier-domain cross-correlation for efficient event-stream matching.
This paper reveals that current video benchmarks often fail to evaluate audio-visual reasoning due to over-reliance on visual cues, but demonstrates that integrating speech encoders with efficient token compression significantly improves performance on tasks requiring speech comprehension and cross-modal grounding.
This paper introduces Fast Image-to-Neural Surface (FINS), a lightweight framework that efficiently reconstructs high-fidelity implicit surfaces and SDF fields from a single image within seconds by leveraging multi-resolution hash grids and pre-trained foundation models, outperforming existing methods in speed and accuracy for robotics applications.
This paper introduces RetoVLA, a lightweight Vision-Language-Action model that enhances spatial reasoning and real-world robotic performance by repurposing discarded register tokens to inject global spatial context into the action-planning module without increasing parameter counts.
This paper introduces QuantVGGT, the first quantization framework for billion-scale Visual Geometry Grounded Transformers (VGGTs), which overcomes unique calibration and distribution challenges through Dual-Smoothed Fine-Grained Quantization and Noise-Filtered Diverse Sampling to achieve significant memory and speedup gains while maintaining high reconstruction accuracy.
This paper presents an autonomous docking framework for UAVs and quadruped robots in GPS-denied, complex terrains, utilizing a deep reinforcement learning-based posture stabilization system for the ground robot and a three-phase, constraint-aware control strategy for the UAV to achieve successful landings on steep slopes and uneven surfaces.