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...
Shape-Adapting Gated Experts (SAGE), an input-adaptive framework that enables dynamic expert routing in heterogeneous visual networks, establishes a scalable foundation for dynamic expert routing in visual networks, thereby facilitating flexible visual reasoning.
G. Thai, Hoang-Nguyen Vu, A. Phan et al.· 2026 IEEE/CVF Conference on...· 1 citation
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...
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
ChatBEV-QA is introduced, a large-scale BEV VQA benchmark of 137K+ QA pairs, designed to evaluate global scene understanding, vehicle-lane interactions, and vehicle interactions within complex traffic environments.
Qing-Yao Xu, Ya Zhang, Yan-Feng Wang et al.· 6 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.
It is proved that automatically generated domain-specific datasets lead to better pretraining than large-scale supervised datasets such as ImageNet-1k and ImageNet-21k and proves the superiority of PaS datasets over existing traditional domain-specific datasets in terms of diversity, scale, and effectiveness.
Jes'us M. Rodr'iguez-de-Vera, Imanol G. Estepa, Ignacio Saras'ua et al.· Pattern Recognition· 5 citations
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
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.
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