7 resultados para "diffusion"
Geração de Imagem: O Guia dos Modelos de Difusão
Geração de ImagemEntenda a geração de imagem por IA: da era das GANs aos modelos de difusão como Stable Diffusion e FLUX, como funcionam, prompts, ControlNet, usos e dilemas éticos.
Como Funciona a Difusão (Sem Matemática)
Geração de ImagemA difusão explicada sem fórmulas: partir do ruído e limpá-lo passo a passo, como o modelo é treinado e o papel do texto e da latent diffusion.
DiffusionGemma: o Google aplica difusão à escrita de texto
MultimodalEm vez de produzir uma palavra por vez, o novo modelo aberto do Google revela blocos inteiros de texto de uma só vez — e passa de 1.100 tokens por segundo. O preço é uma troca explícita de precisão por velocidade.
Hierarchical Pooling for Sheaf Neural Networks
Geração de ImagemarXiv:2606.20932v1 Announce Type: new Abstract: Sheaf Neural Networks (SNNs) generalize Graph Neural Networks (GNNs) by replacing scalar node signals with stalk-valued signals and by using restriction maps to measure compatibility across edges. Unlike standard graph diffusion, which encourages neighboring node features to become similar, sheaf diffusion promotes consistency through the restriction maps and can therefore model more general relationships between neighboring nodes. However, existin...
JPPD: Joint Prediction_Planning Diffusion with Differentiable Safety Guidance for Dynamic Obstacle Avoidance in Intelligent Transportation Systems
Geração de ImagemarXiv:2606.20686v1 Announce Type: new Abstract: Shared-space transportation operation requires low-speed autonomous platforms to navigate safely and efficiently among pedestrians, service robots, micromobility users, carts, and other road users. Most existing systems decompose this problem into trajectory prediction followed by motion planning, which creates one-way information flow: predicted participant futures influence the robot plan, but the selected robot plan cannot influence the predicte...
BayesFP: Posterior Estimation for Flow-Based Policies via Feynman-Kac Sampling
Geração de ImagemarXiv:2606.21014v1 Announce Type: new Abstract: Robots must generate trajectories that remain faithful to learned expert behavior while satisfying safety constraints and task-specific objectives specified only at inference time. We formulate constrained trajectory generation for pretrained diffusion and flow-matching policies as Bayesian posterior sampling, with the learned demonstration distribution as a prior and an inference-time, cost-derived likelihood tilting it toward feasible, optimal tr...
Factor-Aware Mixture-of-Experts with Pretrained Encoder for Combinatorial Generalization
Robótica & RLarXiv:2606.21100v1 Announce Type: new Abstract: The integration of pretrained encoders with diffusion policies has become a dominant paradigm for visual robotic manipulation. However, it still struggles to generalize across complex environments with varying factors such as lighting and surface textures. To address this, we propose FAME, a framework that integrates a factor-aware mixture-of-experts (MoE) with a pretrained encoder to enhance generalization to environmental variations. FAME follows...