Results across multiple time intervals demonstrate the framework's ability to identify high-risk regions with sub-second temporal sensitivity and real-time edge throughput, generating 800 x 800 logarithmic heatmaps at an average of 2.68 FPS.
S. Chaudhuri, A. Jahangiri, Christopher Paolini· arXiv.org· 0 citations
MM++ (Multilayer Mahalanobis++), a strictly post-hoc, and scale-invariant framework for Out-of-Distribution (OOD) detection, constructs a principled joint feature space that captures latent cross-layer correlations while mitigating early-layer noise.
Rahim Hossain, Mahrukh Bhuian, Md. Farhan Shadiq et al.· 0 citations
This central contribution is a tested explanation suite: for a strong ensemble member, part-masking and counterfactual edits show that its decisions depend on motion-grounded body-region evidence, and this region saliency aligns with rule-based Laban Movement Analysis (LMA) attributes far more than with classical kinem...
Naoto Nishida, Yoshio Ishiguro· 2026 14th International Conf...· 0 citations
A deployment-oriented instance segmentation framework for resource-constrained lunar robotics that jointly addresses calibration and system-level fault exposure under strict compute constraints is presented and an integrated approach and a blueprint for a reliable and safe AI perception framework under space deployment...
Siddhant Shete, Hilmi Doğu Küçüker, Udo Frese et al.· 0 citations
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This work proposes a perceptually regularized diffusion framework that incorporates prior knowledge through perceptual-loss-based regularization, improving training convergence and encouraging the recovery of meaningful image features.
Chuxiang Wang, Pavithra Venkatachalapathy, Ying Liang et al.· 0 citations
LF-MultiDiffusion is proposed, a training-free panorama generation method that extends MultiDiffusion to support linear projections between target and reference image spaces and achieves better visual quality, text alignment, and panoramic consistency than the strongest training-free baseline, while providing a 15.36%...
Evaluations show that ranking WSIs based on predictions from zero-shot classification using WSI FMs accurately identifies slides with the most tumor, indicating that WSI FMs contain sufficient morphology signal to automatically triage slides.
Ayushi Sinha, Shashank Yadav, Benjamin Holmes et al.· 0 citations
A leakage-free evaluation protocol linking spatial separation to the model's receptive field is introduced, which shows that Macro-F1 drops by 0.147 on average and model rankings change by as many as five places, and reveals that all ten architectures misclassify largely the same pixels.
Ehsan Faghih, Fatemeh Ashrafi, Marguerite Moore et al.· 0 citations
FAIRLENS shows that fair high-stakes VLM behavior requires similar treatment across groups and refusal to infer high-stakes attributes from appearance, and its question suite transfers to any face corpus with demographic annotations.
Vahid Reza Khazaie, Ahmed Y. Radwan, Shaina Raza· 0 citations
Vision models deployed on microcontrollers (MCUs) are quantized to integer-only arithmetic and run in inference-only runtimes that do not carry the machinery backpropagation needs: the standard tool for adapting a model to the distribution shift (sensor noise, blur, lighting) it meets in the field. Existing forward-onl...
Muhammad Rehan, Haider Ali, Muhammad Ali Munir et al.· 0 citations
Autonomous and intelligent transportation systems operate in complex urban environments where safety depends on interactions among vehicle behavior, environmental conditions, and vulnerable road users (VRUs) such as pedestrians and cyclists. Most advanced driver assistance systems (ADAS) employ reactive mechanisms that...
Discrete visual tokenization, predominantly driven by vector, scalar, and product quantization, lacks a unified conceptual framework for understanding quantization tradeoffs. In this paper, we propose a unified rate--distortion perspective on modern discrete visual tokenization. By viewing quantization as lossy compres...
Xianghong Fang, Wenlong Mou, Yuan Yuan et al.· 0 citations
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.
