5561 papers
This paper introduces the Always-on Egocentric (AoE) system, a low-cost, scalable data collection framework that leverages distributed human agents and smartphones to generate high-quality egocentric interaction data for training embodied AI foundation models.
This study demonstrates that in the context of extreme data scarcity for prodromal Parkinson's disease detection using fMRI, enforcing strict subject-level evaluation reveals severe information leakage in standard image-level splits and shows that lightweight models like MobileNet V1 generalize more reliably than deeper architectures.
This paper introduces the Certainty-Validity (CVS) Framework, a diagnostic tool for discrete commitment systems that exposes the critical flaw of standard accuracy metrics by distinguishing between appropriate uncertainty and harmful confident hallucinations, ultimately arguing that effective training for reasoning systems should prioritize maximizing the CVS score to prevent models from overcommitting to ambiguous data.
This paper presents an open-source tool that automates the quality assurance of multi-sensor railway training data by detecting nine common annotation errors with high precision, thereby significantly reducing manual workload and accelerating the development of AI-driven automated driving systems.
This paper introduces Multimodal Modular Chain of Thoughts (MMCoT), a cost-efficient framework utilizing Vision-Language models to improve automated Energy Performance Certificate (EPC) pre-assessment by decomposing the estimation into structured reasoning stages, which demonstrated statistically significant accuracy gains over standard prompting on a UK residential dataset.
This paper proposes VoxelDiffusionCut, a novel method that leverages a diffusion model to iteratively estimate internal 3D structures from observed cutting surfaces and plan non-destructive cuts, thereby enabling the safe extraction of target components like batteries and motors from complex products by effectively capturing predictive uncertainty to avoid erroneous damage.
This paper proposes the Multi-scale Spatial Adaptive Attention Network (MSAAN), a lightweight image super-resolution framework that integrates novel modules for multi-scale feature aggregation and spatial adaptive attention to achieve superior reconstruction fidelity with significantly reduced computational complexity compared to state-of-the-art methods.
The paper introduces BiSe-Unet, a lightweight dual-path U-Net architecture that combines an attention-refined context path with a shallow spatial path and a depthwise separable decoder to achieve real-time, high-precision medical image segmentation on resource-constrained edge devices like the Raspberry Pi 5.
NovaLAD is a fast, CPU-optimized document extraction pipeline that leverages concurrent YOLO models, rule-based grouping, and selective Vision LLM processing to convert unstructured documents into structured formats with state-of-the-art accuracy on the DP-Bench benchmark, enabling efficient Generative AI applications without requiring GPU hardware.
This paper introduces CT-Flow, an agentic framework that leverages the Model Context Protocol to transform static 3D CT analysis into a dynamic, tool-mediated workflow, achieving state-of-the-art performance on the newly curated CT-FlowBench by autonomously orchestrating complex diagnostic tasks through iterative tool use.
QuickGrasp is a responsive, QoS-aware system that bridges the accuracy-latency trade-off in video-language querying by employing a local-first architecture with on-demand edge augmentation, shared vision representations, and adaptive tokenization to match large model performance while significantly reducing response delays.
This paper presents an unsupervised, self-annotation methodology using superpixel algorithms and CNNs to effectively segment low-contrast X-ray computed tomography scans of concrete, addressing the challenge of obtaining labeled training data for distinguishing aggregates from mortar.
This study evaluates the efficacy of using urban morphometrics, alone and fused with satellite imagery, to predict Local Climate Zones across five sites, finding that while morphometrics capture a broader range of urban form properties, the predictive relationship is site-dependent and inconsistent, suggesting the LCZ framework should be used with caution in morphological studies.
This paper introduces GUARD, a novel inference-time framework that mitigates memorization in text-to-image diffusion models by dynamically adjusting the denoising process through statistical identification and attenuation of cross-attention, effectively preventing the reproduction of training data while maintaining high image quality and prompt alignment.
TinyVLM is the first framework to enable zero-shot object detection on memory-constrained microcontrollers by leveraging a decoupled architecture, Matryoshka distillation, and quantized embeddings to achieve real-time inference with less than 1MB of total memory.
This paper introduces Latent Replay Detection (LRD), a memory-efficient continual object detection framework for microcontrollers that utilizes task-adaptive FiLM-based compression and spatial-diverse exemplar selection to enable learning new object categories within strict 64KB memory budgets.
This paper presents TerrAI, a neural network-based solution that utilizes multispectral images to enable accurate, data-driven Targeted Spraying and Fertilization in wheat fields by accounting for spatio-temporal variability across different parcels.
The paper proposes Reachability-Aware Diffusion Steering (RADS), an inference-time framework that leverages reachability analysis and constrained reinforcement learning to steer diffusion models away from memorized training data while preserving generation quality and prompt alignment.
This paper proposes ADE-CoT, an adaptive test-time scaling framework that enhances image editing efficiency and performance by dynamically allocating resources based on edit difficulty, employing edit-specific early verification, and utilizing depth-first opportunistic stopping to achieve superior results with significantly reduced sampling budgets compared to existing methods.
GrapHist is a novel, parameter-efficient graph-based self-supervised learning framework that models histopathology images as cell graphs to capture biological heterogeneity, achieving competitive performance across multiple downstream tasks while outperforming fully-supervised graph models and establishing the first large-scale graph benchmark in digital pathology.