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3,080 papers
DeepFedNAS is introduced, a two-phase framework built on a multi-objective fitness function that synthesizes information-theoretic network metrics with architectural heuristics that makes DeepFedNAS practical for scalable, communication-constrained IoT federations.
This work formalizes Open-Vocabulary Domain Unlearning (OVDU), a rigorous protocol that mandates domain forgetting must transfer to held-out classes and proposes a surgical parameter-editing framework that vastly improves open-vocabulary generalization over existing baselines.
Cross-domain few-shot learning requires adapting a classifier to a new visual domain from very few labelled examples without target-time parameter updates. We isolate one question: under a fixed global representation, what does joint query-support adaptation contribute to prototype construction? The Within-Instance Pro...
StarWM is proposed, which uses a cross-attention module trained on self-supervised dynamics to decide where reconstruction applies and preserves state attributes with near-perfect fidelity through long-horizon imagination while systematically discarding distractors.
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Results show that lightweight channel-aware adaptation can recover a substantial fraction of the robustness of a much larger communication interface while broadening quality-valid operation under constrained wireless conditions.
Theoretical analysis shows that the fixed-point distributions of RWTD interpolate between off-policy reward tilting of the reference and on-policy tilting of the current model, providing a principled approach to balancing reward adaptation with retention of prior knowledge.
Quat-Sphere-Vision (QSV), a sparse spherical vision model that replaces this projection triple with a single learned unit quaternion per token, is studied, showing the two roles to be asymmetric.
The approach, called Unified Dynamic FLD, streamlines the landmark detection pipeline by enabling a single model to learn on any number of ``N$-point''datasets, and yield any number of specific landmark predictions by loading the designated landmark queries at runtime.
DyMoS (Dynamic Motion Slider), a training-free and model-agnostic method that rebalances the attention pathway from generated frames to the reference frame during initial denoising steps, is proposed and demonstrated to improve motion dynamics while maintaining visual quality and fidelity to the reference image.
Concentrate and Concentrate (CaC) is a coarse-to-fine anomaly reward model based on Vision-Language Models that first conducts a global temporal scan to anchor anomalous time windows, then performs fine-grained spatial grounding within the localized interval, and finally derives robust judgments via structured spatiote...
This work proposes Generative Video Codebook Codec (GVCC), a zero-shot framework in which a pretrained video generative model serves directly as the decoder, and the transmitted bitstream specifies its generation trajectory.
Modern vision-language models struggle with basic compositional reasoning, failing to bind attributes to objects or relations to their referents. Existing benchmarks either rely on noisy real images that conflate confounding visual variables with the reasoning failure, or use simplistic synthetic scenes lacking the rea...
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.
Human-Computer Interaction and Visualization
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