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4,009 papers
A two-step strategy for correcting ML-based state estimation, where an offline computation is used to characterize the uncertainties resulting from the ML module's state estimation, using preimages of perception contracts at runtime, using a risk heuristic.
Experiments show that PRQuant substantially reduces down-projection reconstruction error, with scaling and residual compensation providing the main numerical gains while permutation enables a hardware-friendly contiguous layout.
Experimental results on the AVI Challenge 2026 validation set show that this framework reduces mean squared error (MSE) by approximately 25% compared with the baseline, and consistent improvements on the official test set demonstrate that the method achieves the best performance.
A model that predicts this transformation in a single forward pass is learned: a MobileNetV4 backbone with FiLM-based emotion conditioning outputs parameters for differentiable global transformations, which reduced viewer-reported arousal relative to unedited images, comparably to the grayscale well-being filter.
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Experiments on BEAT2 show that GestureFAR significantly improves the quality--latency trade-off among streaming-capable methods, preserving strong gesture quality while enabling real-time token-causal generation.
This work advances SPI research from static images to dynamic videos and ensures more comprehensive scenario coverage during benchmarking with RobotEQ-Video, a hierarchical world-state taxonomy organized into a four-level coarse-to-fine structure.
CharBM25 is established as an effective, GPU-free retrieval strategy that matches or exceeds dense retrieval at negligible computational cost, and combining it with a general-purpose LLM of 12B+ parameters delivers reliable, training-free Devanagari post-OCR correction without task-specific fine-tuning.
Omni Demand Understanding is established as a distinct multimodal contextual inference problem: given an interaction stream, a model must detect whether a user demand is present and infer intent from multimodal and conversational context, and ODU is evaluated along five dimensions, covering both single-turn and multi-t...
This work proposes MME-Safety, a rigorously verified benchmark featuring a unique four-dimensional annotation schema that categorizes risk scenarios, harm severity, and modality-specific stealth levels and introduces a hierarchical evaluation framework to assess fundamental response reliability, actual risk exposure, a...
PECT consistently improves reconstruction performance across multiple phoneme-based visual speech recognition frontends, including visual automatic speech recognition (V-ASR), point visual automatic speech recognition (PV-ASR), and head-pose-aware visual speech recognition (HP-VSR) variants.
This work proposes a diagnostic test for class sensitivity: a method’s ability to distinguish between competing class labels on the same input, and introduces CASE, a contrastive explanation method that isolates features uniquely discriminative for the predicted class.
This paper introduces the Polygons Transformer (PoT), a novel architecture that encodes 2D continuous vector geometries while allowing cross-polygon attention and couple this novel architecture with a Combinatorial Optimization Reinforcement Learning (CORL) training framework to find optimal solutions.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
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.
A Blog post by H company on Hugging Face
A Blog post by Liquid AI on Hugging Face
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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