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3,740 papers
PACE (Pixel-Adaptive Condense and Extract), a training-free inference framework that accelerates both the vision encoder and the Large Language Model (LLM) via a unified Condense-and-Extract paradigm, is proposed.
A dual-branch controlled framework is constructed in which both a Vision-based model and an index-based baseline share an identical decoder backbone, training objective, optimizer, and data curriculum, suggesting that transformers are more modality-agnostic than commonly assumed, and that discrete tokenization is not a...
Federated Continual Multimodal Learning (FedCMM), a framework that embeds continual-learning safeguards into the federated optimization loop at three complementary levels, is proposed, confirming that holistic, modality-aware optimization enables robust evolutive adaptation across heterogeneous networked AI deployments...
This work examines whether a robot's action representations preserve the semantic structure captured by pretrained encoders and introduces a plug-and-play method that anchors action representations to a semantic manifold while decomposing representations into a shared semantic channel and a private channel, all discard...
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This work introduces DRIFT (Denoising Redirection via Input perturbation of the Flow-matching Trajectory), a test-time universal adversarial patch placed on the robot's gripper that attacks the denoising velocity field of an off-the-shelf policy.
A simulator-grounded construction framework, instantiated as 3DTongueQA, that shows geometry-grounded supervision supporting both unified language QA and structured prediction and shows geometry-grounded supervision supporting both unified language QA and structured prediction.
The SOPHIAS dataset, a 12-hour multimodal dataset containing recordings of 50 oral presentations delivered by 65 undergraduate and master's students at the Universidad Autonoma de Madrid, enables the exploration of relationships between multimodal behavioral and physiological signals and presentation performance, and p...
SeMoCo, a semantic-first motion codec, is introduced together with a dual-axis motion generator for language-conditioned motion generation and $\Omega$-MotionVerse, a large-scale, multi-source human-motion dataset unified under the SOMA representation is constructed.
MemoryCard is a video-memory-based augmentation framework that organizes long videos into self-contained Memory Cards, each corresponding to a distinct topic or event, and consistently improves long-video QA performance under comparable visual-token budgets.
Mean Cumulative Drift (MCD), an embedding-based measure of content retention across three representation spaces, and Multi-Generation GenEval (MGG), extending GenEval's object-level compliance scoring across generations are proposed, to quantify drift.
Maltese has substantial text corpora and pretrained language models, but paragraph-scale OCR training data remains scarce; NOMOCRAT provides 57 verified annotated pages. LV-ROVER-MLT combines synthetic fine-tuning of Tesseract~5 with five complementary recognition streams and lexicon-gated word-level arbitration adapte...
This work studies ViT attention heads and finds they differentiate into object- and background-specialist roles, a pattern most pronounced under full attention, and proposes SHS-Index to quantify this specialization, showing that it distinguishes full-attention from chunk-window ViTs, and finds that it strongly tracks...
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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