The Very Big Video Reasoning (VBVR) Dataset is introduced, an unprecedentedly large-scale resource spanning 200 curated reasoning tasks following a principled taxonomy and over one million video clips, approximately three orders of magnitude larger than existing datasets.
Maijunxian Wang, Rui-Si Wang, Ju-Yi Lin et al.· arXiv.org· 28 citations· ⚡5
This work introduces anti-aliasing strategies using ASAP and BlurPool to significantly improve explanation quality and preserves strong predictive performance while providing faithful and artifact-free explanations suitable for clinical application in multi-class and multi-label settings.
Shreyash Arya, Shashank Agnihotri, Marcel Kleinmann et al.· 0 citations
The integrated multivariate segmentation tree (IMST), a comprehensive framework designed to improve credit evaluation for small- and medium-sized enterprises (SMEs) by integrating financial data with textual sources, demonstrated superior interpretability and computational efficiency.
Lu Han, Xiuying Wang· Expert systems with applicat...· 2 citations
It is shown that using the standard velocity prediction in flow matching in this high-dimensional space requires the model to fit orthogonal noise directions outside the low-dimensional signal manifold, making optimization inefficient, and motivated using the clean data parameterization ($\boldsymbol{x}_{0}$-prediction...
Chao Feng, Zhi-Yang Xu, Bo-Wei Chen et al.· 0 citations
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This paper builds upon the large-scale dataset pairing MetOp radiances with ERA5 PBLH labels, and presents the best-performing architecture found: a dual-encoder Transformer whose masked-input handling lets it operate in all weather conditions.
Lorenzo Innocenti, Luca Catalano, Edoardo Arnaudo et al.· 0 citations
K-Means clustering is addressed with K-Means clustering, achieving near-lossless accuracy and a mean speed-up over the full-precision model, and it is revealed that excluding from quantization the layers whose speed-up is negligible, regardless of their sensitivity, can be counterproductive.
D. Población-Criado, D. García-Gasulla, Eduardo Quiñones· 0 citations
Robotic assembly in high-mixture settings requires adaptable systems that can handle diverse parts, yet current approaches typically rely on policies specialized to each insertion task. Although this can reach high success rates, it makes the process of deploying systems for new problems tedious and time consuming. We...
Nicklas Hansen, Iretiayo Akinola, Yi-Jie Guo et al.· 0 citations
Visual Tripwires is introduced, a predictive reliability framework that uses temporal instability in model behaviour to anticipate impending failure and demonstrate that temporal instability contains useful information about future model reliability and provides a practical basis for early warning in deep vision system...
Anoushka Harit, Rehan Zuberi, William Prew et al.· 0 citations
The Spectral Pullback Network (SPN) is developed, which learns a low-rank version of the metric from randomized power iteration, and is distill into a $310$K-parameter importance head that predicts token importance directly from the features.
Andrew Bond, Ege Erdem Ozlu, Tuna Çimen et al.· 0 citations
Implicit neural representations (INRs) have emerged as a promising paradigm for video compression, providing compact neural representations with flexible spatial and temporal reconstruction. Hierarchical grid-based architectures such as HiNeRV achieve strong rate--distortion performance, but require extensive per-video...
Naser Alizada, Farhang Baghban, Hashem Pishkar et al.· 0 citations
It is suggested that combining complementary pretrained representations can improve histopathological classification when annotations are limited, and FFM-CP achieves higher mean macro-F1 than the strongest individually adapted member of each fused set in 50 of 54 comparisons.
Anh-Tien Nguyen, Trung DQ. Dang, N. T. Diep et al.· 0 citations
M3D-Net is presented, a mammography encoder that hierarchically coordinates multi-scale coordinate attention, bounded dynamic feature reuse, and differential attention through resolution-aware operator placement that supports further evaluation of hierarchical coordination across breast imaging settings.
Zheng Yu, Xin-Hang Li, Jia-Bao Gao 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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