The authors' analysis identifies temporal modeling as the architectural factor most consistently associated with generalization to the unseen center, although the effect is not uniform across metrics in the controlled baseline comparison, and both parameter-efficient baselines remain more costly than a constant predict...
Max Kirchner, Hanna Hoffmann, Alexander C. Jenke et al.· Medical Image Analysis· 1 citation
Recent success in contrastive learning has sparked growing interest in more effectively leveraging multiple augmented views of data. While prior methods incorporate multiple views at the loss or feature level, they primarily capture pairwise relationships and fail to model the joint structure across all views. In this...
The effectiveness of large-scale GMMs on the task of zero-shot image denoising, where sublinear training results in state-of-the-art denoising times while competitive denoising performance is maintained, is demonstrated.
S. Salwig, Till Kahlke, F. Hirschberger et al.· arXiv.org· 1 citation
Deep neural networks (DNNs) rely heavily on high-quality open-source datasets (e.g., ImageNet) for their success, making dataset ownership verification (DOV) crucial for protecting public dataset copyrights. In this paper, we find existing DOV methods (implicitly) assume that the verification process is faithful, where...
Ting Qiao, Yiming Li, Jianbin Li et al.· 0 citations
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This framework outperforms existing methods across diverse benchmarks, particularly in long-tailed scenarios, providing a robust baseline for real-world OOD detection and boosting both OOD detection performance and classification accuracy.
Solving novel view synthesis (NVS) for millimeter-wave (mmWave) radar requires a renderer that is physically faithful, complex-valued, and multi-viewpoint-tractable. No prior method achieves these three properties simultaneously. Differentiable Monte Carlo (MC) ray tracers implement the radar forward model directly wit...
A. Armouti, Yixuan Gao, Rajalakshmi Nandakumar· 0 citations
The Logit Refiner is introduced, a lightweight autoregressive module that restores intra-scale dependencies by sequentially sampling tokens conditioned on frozen backbone features and generalizes to text-to-image generation, confirming that the mean-field bottleneck persists across VAR variants and is effectively allev...
Meimingwei Li, S. Baumann, F. Krause et al.· 0 citations
Automated plankton imaging produces severely long-tailed datasets, where the rare taxa of greatest ecological interest have too few images to train or evaluate classifiers reliably. We generate synthetic plankton imagery conditioned on taxonomy: a CLIP encoder is adapted on a large plankton corpus with a ranked contras...
Daniela Ivanova, Ozgu Goksu, Nicolas Pugeault· 0 citations
Vidu S2, which comprises Vidu S2-Avatar, a real-time interactive digital-character model, and Vidu S2-Editing, a real-time video editing model, is presented and the feasibility of real-time spatial video generation for both Vidu S2-Avatar and Vidu S2-Editing is explored.
Jintao Zhang, Kai Jiang, Jin-Tao Chen et al.· 3 citations
Inspired by Mixture-of-Experts (MoE) principles, Evolvable-Substrate HyperNEAT is partitioned into non-overlapping spatial segments, each assigned to a separately evolved specialist network, which forces evolution to discover features across the entire image.
Romain Claret, Arthur Gygax, Michael O'Neill et al.· 2 citations
We introduce Complex-Valued Quadratic Phase Gaussian (CVQPG), a novel hologram representation method that augments each 2D Gaussian primitive with a quadratic phase profile controlled by a learnable curvature parameter. Against the planar Gaussian baseline, CVQPG improves the average PSNR of holographic reconstructions...
Hao-Long Wang, Yicheng Zhan, K. Akşit et al.· 0 citations
Modern VLMs already encode much of what is needed for specialized domains; the challenge is learning how to ask, suggests modern VLMs already encode much of what is needed for specialized domains; the challenge is learning how to ask.
Gautam Rajendrakumar Gare, Si-Ying Li, He-Wei Wang et al.· 1 citation
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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