A physics-informed self-distillation framework built upon the DINOv3 architecture utilizing a ConvNeXt-v2-Tiny backbone to maximize data efficiency and enforces view-invariance, and proposes a dense, hierarchical feature fusion strategy across all four network stages to preserve fine-grained sediment details alongside...
Taqi Hamoda, Hayat Rajani, Nuno Gracias· 0 citations
WoundAIssist enables patients to document wounds at home via photographs and questionnaires, while physicians remain engaged in the care process through remote monitoring and video consultations, and synthesizes design insights for remote patient monitoring apps that may inform the development of similar digital health...
Vanessa Borst, Anna Riedmann, Tassilo Dege et al.· ACM Transactions on Computin...· 6 citations
This work introduces Data Journalist Agent (Data2Story), a multi-agent framework that orchestrates specialized roles into a single virtual newsroom, enabling more evidence-based, transparent, and verifiable reporting.
K. Lin, EI Batu, Yu-Hong Shi et al.· arXiv.org· 0 citations
Redemption Score is introduced, a novel evaluation framework for multi-modal generation by triangulating three complementary signals: Mutual Information Divergence for global image-text distributional alignment, DINO-based perceptual similarity of cycle-generated images for visual grounding, and LLM Text Embeddings for...
Ashim Dahal, Ankit Ghimire, Saydul Akbar Murad et al.· 0 citations
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A Retrieval-Augmented Generation framework for scientific image quality assessment, designed to simultaneously address both the understanding track (SIQA-U) and the scoring track (SIQA-S) of the SIQA challenge, is proposed.
Yi-Nuo Zhang, Bing-Shuo Liu, Zhi-Ying Tu et al.· 0 citations
Ambivalence and hesitancy (A/H) are affective states in which individuals express contradictory signals across facial, vocal, and linguistic channels. Automatically recognising A/H in clinical videos requires detecting cross-modal disagreement -- the signal that standard fusion methods suppress. Based on the conflict-a...
Shiyu Luo, Yu Wang, Jiawen Huang et al.· 0 citations
This work compares convolutional neural network, vision transformers, and hierarchical Swin transformers using the same three-channel jet-image representation, consisting of charged-particle momentum, neutral-particle momentum, and charged-particle multiplicity from PYTHIA 8 jets.
Daeun Kim, Jiwon Lee, Wonjun Jeong et al.· Physica Scripta· 0 citations
The Spatial Relation Recognition Task (SpaRRTa) benchmark is introduced, which evaluates the ability of VFMs to identify relative positions of objects in the image and provides insights into the mechanisms that support or hinder spatial awareness in modern VFMs.
Turhan Can Kargin, Wojciech Jasinski, Adam Pardyl et al.· arXiv.org· 0 citations
This work proposes OPTED (on-policy fine-tuning for end-to-end driving) which decouples reinforcement learning from the post-training of the end-to-end policy: a privileged teacher is trained using RL on vectorized inputs (HD-map and bounding boxes) which provides supervision to the pre-trained student during closed-lo...
D. Da Col, M. Igl, Peter Karkus et al.· 1 citation
The results support the conclusion: foundation-model supervision can amplify a fixed manual annotation budget into a substantially larger training set and yield a compact, deployable edge model.
Fabian Schmalstieg, Karsten Mueller, Wojciech Samek· 0 citations
Magnetic Resonance Imaging (MRI) acquired at different field strengths exhibits pronounced variation in noise, resolution, homogeneity, and contrast, which limits comparability across acquisition settings and complicates downstream analysis. We address this with a unified conditional model for controllable field-to-fie...
Siddharth Srivastava, Till Bretschneider· 0 citations
TinyCNN is thus an energy-efficient, deployable building block for sustainable agricultural intelligence, while field robustness remains the key barrier to durable real-world impact.
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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