MedSegLatDiff is proposed, a diffusion based framework that combines a variational autoencoder (VAE) with a latent diffusion model for efficient medical image segmentation that provides enhanced interpretability and reliability compared to deterministic baselines, making the model particularly suitable for clinical dep...
Ngoc Huynh Trinh, H. Nguyen, S. B. Lương et al.· arXiv.org· 0 citations
As we deploy machine learning systems in the real world, a core challenge is to maintain a model that is performant even as the data shifts. Such shifts can take many forms: new classes may emerge that were absent during training, a problem known as open-set recognition, and the distribution of known categories may cha...
The significant variability in cell size and shape continues to pose a major obstacle in computer-assisted cancer detection on gigapixel Whole Slide Images (WSIs), due to cellular heterogeneity. Current CNN-Transformer hybrids use static computation graphs with fixed routing. This leads to extra computation and makes i...
Gia Huy Thai, Hoang-Nguyen Vu, Anh-Minh Phan et al.· 0 citations
A world model is a cognitive simulator of the real-world environment allowing biological agents to reason about how the world evolves, whether spontaneously or in response to their actions, and accordingly to plan and strategize. In building Artificial Intelligence (AI) systems, world models represent the next frontier...
PAN Team, Zihan Liu, Yi Gu et al.· 0 citations
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DC-Gen, a general framework that accelerates text-to-image diffusion models by leveraging a deeply compressed latent space, uses an efficient post-training pipeline to preserve the quality of the base model to reduce the latency of 4K image generation.
Convolutional Neural Networks have become a cornerstone of medical image analysis due to their proficiency in learning hierarchical spatial features. However, this focus on a single domain is inefficient at capturing global, holistic patterns and fails to explicitly model an image's frequency-domain characteristics. To...
Md. Saiful Bari Siddiqui, Mohammed Imamul Hassan Bhuiyan· 0 citations
FloodVision is proposed, a knowledge-guided framework for estimating flood depth from a single RGB image that integrates a general-purpose VLM with FloodKG, a domain knowledge base encoding canonical object dimensions and component landmarks to encourage reasoning at the component level rather than treating objects as...
Zhangding Liu, Neda Mohammadi, John E. Taylor· 4 citations
Evaluation on the RescueNet dataset, which includes 4,494 UAV images collected after Hurricane Michael and annotated with 10 damage categories, shows that MCANet achieves the highest mean average precision (mAP) among the evaluated models, with an mAP of 91.37%.
Zhangding Liu, Neda Mohammadi, John E. Taylor· arXiv.org· 0 citations
GlobeReady is developed, a deployment-oriented platform powered by the RetiGlobe model and local feature augmentation that evaluates domain generalisability, Bayesian uncertainty quantification, out-of-distribution (OOD) detection, and feature-based case retrieval in medical imaging diagnostics.
Meng Wang, Tian Lin, Qing-Shan Hou et al.· 1 citation
Comprehensive traffic scene understanding is a foundational capability for Intelligent Transportation Systems (ITS) underpinning applications such as traffic simulation. While VisionLanguage Models (VLMs) have demonstrated strong reasoning potential, their application to Bird's-Eye View (BEV) maps in traffic contexts r...
Qingyao Xu, Ya Zhang, Yanfeng Wang et al.· 0 citations
Vision and Language Navigation (VLN) requires an embodied agent to traverse complex environments by following natural language instructions, demanding accurate alignment between visual observations and linguistic guidance. To address these challenges, we propose a fine grained instruction guided graph reasoning framewo...
Yaohua Liu, Binkai Ou, Rong Fu et al.· 0 citations
This study establishes DUA-D2C as a theoretically grounded and effective approach to combating overfitting in modern deep learning, analytically demonstrating how dynamic parameter fusion reduces model variance.
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
