6052 papers
This paper proposes Inter-image Pixel Shuffling (IPS), a novel multi-focus image fusion method that synthesizes training data by shuffling pixels between clear and low-pass filtered images to enable deep learning models to learn fusion without real multi-focus datasets, while utilizing a hybrid cross-image network combining CNNs and state space models to achieve superior fusion quality.
This paper proposes a data-driven initialization strategy for autonomous racing trajectory optimization that utilizes a neural network trained on Formula 1 telemetry to predict expert-like raceline offsets, thereby significantly accelerating solver convergence and reducing runtime compared to traditional geometric baselines while maintaining optimal lap times.
This paper introduces a comprehensive multimodal dataset comprising audio and vibration signals from a single-speed chain conveyor system, designed to benchmark robust industrial fault detection and classification under diverse operating conditions and noise levels through standardized evaluation protocols and baseline models.
This paper introduces EyExIn, a data-efficient framework that enhances retinal Vision Language Models by employing a dual-stream encoding strategy and a deep expert injection mechanism to bridge perception and reasoning gaps, thereby achieving state-of-the-art precision in ophthalmic diagnosis while preventing hallucinations.
This paper argues that human-AI alignment in early developmental communities should be treated as a community-governed process involving layered collaboration between families and professionals, rather than an individual optimization problem, by establishing expert-grounded structures, professional guardrails, and family-level adaptations for multimodal LLM outputs.
The paper introduces AutoSelect, a training-free token pruning method for vision-language models that reformulates token selection as capacity-constrained communication using a noise-gating mechanism to identify and retain only the most informative visual tokens, thereby significantly accelerating inference while preserving nearly all model accuracy.
DexKnot is a generalizable visuomotor framework that combines keypoint affordances with diffusion policies to enable robots to reliably knot plastic bags across diverse, unseen instances by learning shape-agnostic representations from real-world manual deformations.
This paper proposes and experimentally validates a model-based method that links actuator expansion to surface temperatures to compensate for thermal drift in high-precision hexapod robots, achieving a reduction in thermally induced errors of over 80%.
The paper proposes PDD, a novel framework that unifies global contextual and local structural priors from dual frozen encoders into a shared manifold to distill diverse knowledge into complementary student networks, achieving state-of-the-art performance in medical image anomaly detection across multiple datasets.
This tutorial provides a control-oriented introduction to aided inertial navigation systems by utilizing a Lie-group formulation based on the extended Special Euclidean group SE(2,3) to establish a clear, geometric framework for fusing inertial and aiding measurements while explicitly leveraging invariance and symmetry principles.
The paper introduces CanoVerse, a massive dataset of 320K canonicalized 3D objects and a high-throughput framework that resolves directional ambiguity to significantly improve 3D generation stability, cross-modal retrieval, and zero-shot orientation estimation.
This paper introduces LiveWorld, a novel framework that addresses the "out-of-sight dynamics" limitation in generative video world models by maintaining a persistent global state where unobserved entities continue to evolve, thereby enabling truly continuous 4D world simulation and long-term scene consistency.
This paper proposes a framework integrating transition state theory with dynamic network modeling to characterize and predict discrete structural changes, such as faction realignment, often using cross-sectional data to forecast future network evolution.
The paper introduces NarrativeLoom, a multi-persona collaborative storytelling system grounded in Campbell's theory of blind variation and selective retention, which a controlled study of 50 participants shows significantly enhances the novelty, diversity, and overall creativity of co-authored stories compared to existing tools, particularly benefiting novice writers through structured scaffolding.
This paper proposes a computationally efficient inference-time reasoning method that improves accuracy by selecting thought-level continuations that maximize the model's internal self-certainty, demonstrating that optimizing for uncertainty minimization at early planning stages yields performance comparable to or exceeding existing scaling techniques like self-consistency.
PromptGate is a dynamic, federated vision-language framework that utilizes adaptive, learnable prompts to effectively filter out-of-distribution noise from unlabeled medical data pools, thereby significantly improving the efficiency and privacy of open-set active learning across resource-constrained institutions.
The paper proposes RoTri-Diff, a diffusion-based imitation learning framework that explicitly models the spatial triadic relationship between two robot arms and an object to generate stable, coordinated bimanual manipulation trajectories, outperforming state-of-the-art baselines in both simulation and real-world tasks.
The paper proposes ACD-U, an asymmetric co-teaching framework that combines a CLIP-pretrained Vision Transformer with a CNN and incorporates machine unlearning to actively correct selection errors and achieve state-of-the-art robustness against noisy labels.
This paper introduces a framework to evaluate class visualizations and activation atlases for transformer-based pathology models, revealing that while these feature visualization methods effectively capture coarse tissue-level concepts, their ability to represent fine-grained cancer subclasses is limited by intrinsic pathological complexity and reduced inter-observer agreement.
This paper proposes using graph neural networks to predict initial branching orders for CDCL SAT solvers, demonstrating significant speedups on random and pseudo-industrial benchmarks while noting that the approach struggles with complex industrial instances due to the solver's dynamic heuristics overriding the predictions.