SGP-SAM, a self-gated prompting framework for efficient and effective transfer to 3D lesion segmentation, and a Zoom Loss that up-weights lesion-focused supervision by combining Dice and a voxel-balanced focal term to address small-lesion learning.
Ze-Quan Yao, Zi-Xuan Tang, Jie Ma et al.· arXiv.org· 0 citations
Medical image generators trained on imbalanced data can fail at demographic intersections absent from training. We introduce CompDiff, which encodes age, sex and race separately and composes supervised demographic tokens alongside clinical text. Across chest radiographs and fundus images, CompDiff improves overall and...
Mahmoud K. Ibrahim, Bart Elen, Chang Sun et al.· 0 citations
A novel guided method is proposed by using the h-transform, a tool that can constrain stochastic processes (e.g., sampling process) under desired conditions and modify the transition probability at each sampling timestep by adding to the original differential equation with a drift function, which approximately steers t...
Yanghao Wang, Zi-Qi Jiang, Zhen Wang et al.· 3 citations· ⚡1
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Remarkably, using a compact RubiCap-3B as a captioner produces stronger pretrained VLMs than those trained on captions from proprietary models, outperforming supervised distillation, prior RL methods, human-expert annotations, and GPT-4V-augmented outputs.
Tzu-Heng Huang, Sirajul Salekin, J. Movellan et al.· arXiv.org· 8 citations· ⚡2
Vision Transformers (ViTs) often degrade under distribution shifts because they rely on spurious correlations, such as background cues, rather than semantically meaningful features. Existing regularization methods, typically relying on simple foreground-background masks, which fail to capture the fine-grained semantic...
This work pretrain decoder-only models with approximately GPT-2 small and medium sizes on the FineWeb-Edu dataset and introduces Deep Delta Learning (DDL), which applies the delta rule over network depth.
Yi-Fan Zhang, Yi-Feng Liu, Mengdi Wang et al.· arXiv.org· 8 citations· ⚡2
Out-of-domain (OOD) robustness is challenging to achieve in real-world computer vision, especially in unsupervised domain adaptation scenarios, where shifts in image background, style, and acquisition instruments often degrade model performance. Generic augmentations show inconsistent gains under such shifts, whereas d...
Ruoqi Wang, Haitao Wang, Shaojie Guo et al.· 0 citations
Vision-Language-Action models (VLAs) represent a significant frontier in embodied intelligence, aiming to bridge digital knowledge with physical-world interaction. Despite their remarkable performance, foundational VLAs are hindered by the prohibitive computational and data demands inherent to their large-scale archite...
Zhaoshu Yu, Bo Wang, Pengpeng Zeng et al.· 0 citations
A new model family (CortexMAE) trained using the masked autoencoder framework is introduced, and in this setting the CortexMAE family outperforms prior models by a large margin, and the first open evaluation suite (Brainmarks) for fMRI foundation models is released.
Connor Lane, Mihir Tripathy, L. Murali et al.· arXiv.org· 5 citations· ⚡1
Recent video generation models have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond their creative potential, such models also hold promise as world simulators for robotics and embodied decision making. Despite strong advances, current approaches still struggl...
David Romero, Ariana Bermudez, Viacheslav Iablochnikov et al.· 0 citations
This work proposes a cross-attention-based aggregation method in which the query projections come from the node, while the key projections come from its neighbors, and introduces a novel architecture called AttentionViG that uses the proposed cross-attention aggregation scheme to conduct non-local message passing.
H. Gedik, Andrew Martin, Mustafa Munir et al.· arXiv.org· 2 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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