This work proposes DesignAsCode, a novel framework that reimagines graphic design as a programmatic synthesis task using HTML/CSS, incorporating a Plan-Implement-Reflect pipeline, incorporating a Semantic Planner to construct dynamic, variable-depth element hierarchies and a Visual-Aware Reflection mechanism that optim...
The results suggest that explicitly regulating representation geometry is an effective complement to optimization balancing, and provide evidence that geometry-aware regularization can improve multimodal learning across diverse architectures and domains.
Zixuan Xia, Hao Wang, Peng-Cheng Weng et al.· 1 citation
Extensive experiments on MS-COCO across three representative diffusion backbones demonstrate that TPSO substantially improves diversity, boosting performance from 1.10 to 4.18, while maintaining image quality with only a modest inference-time overhead.
Debin Meng, Chen Jin, Zheng Gao et al.· 3 citations
This work investigated geo-biases in a Eurocentric driving dataset (Cityscapes) on the fine-grained localization task of instance segmentation and found that instance segmentation models trained on European driving scenes (Eurocentric models) were geo-biased.
Rahul Nair, Gabriel Tseng, Esther Rolf et al.· 2025 IEEE/CVF Conference on...· 0 citations
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CON decomposition is introduced, which quantifies how much of a layer's variance each concept explains given all other concepts and the outcome, and how much none of them explains, yielding layer-comparable, calibrated scores that suppress false positives.
Roshan Prakash, Marco Simnacher, Manuel Pfeuffer et al.· 0 citations
SERUM is the first system to produce interpretable process models from unstructured egocentric screen video without manual annotation, opening a scalable pathway for user modeling and behavioral understanding in the wild.
Andy J. Phu, Karin de Langis, James C Mooney et al.· arXiv.org· 0 citations
The OISD framework is proposed, which improves reasoning by transferring on-policy predictive signals from the final layer to intermediate representations and employs signed advantage-weighted Jensen--Shannon alignment to distill informative intermediate representations while preserving policy consistency under a unifi...
Xin-Yu Liu, Darryl C. Jacob, Yang Zhou et al.· arXiv.org· 0 citations
These results suggest that in diversity-aware multi-armed bandits, e.g., for generative model selection, exploration can arise intrinsically from the objective's geometry, particularly for widely used metrics such as FID and Vendi where tight confidence bounds are difficult to construct.
This paper systematically investigate the memorization behaviors of RF through the test statistics of Membership Inference Attacks (MIA), culminating in a complexity-calibrated metric that successfully decouples intrinsic image spatial complexity from genuine memorization signals.
Mingxing Rao, Daniel Moyer· arXiv.org· 0 citations
MUSE (Multimodal Unified Safety Evaluation), an open-source, browser-based, run-centric platform for multimodal safety evaluation, demonstrates the value of run-centric, fine-grained evaluation for characterizing multimodal safety behavior beyond a single binary success metric.
FACET proposes an efficient replay-free task-conditioned feature consistency loss, aiming to mitigate catastrophic forgetting of the learned mixture distribution in the adapter's feature space, and demonstrates robust scalability.
This paper presents a comparative study of segmentation architectures, ranging from convolutional backbones to vision transformers, applied to the B.O.V.I.D. dataset, a corpus of high-resolution bovid dental photographs paired with hand-made segmentation masks not originally designed for ML-based training, and evaluate...
Keith G. Mills, Evan B. Sanders, Gregory J. Matthews et al.· 0 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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