AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,462 papers
It is demonstrated that pseudo-label quality, not quantity, is a decisive factor in self-training under domain shift, and that cross-modal language grounding offers a practical path to reliable automatic annotation in deployment-critical applications.
A clinically curated Pan-Asia WSI--report dataset is introduced and the REG 2025 benchmark is established as a benchmark for evaluating WSI-based structured report generation and vision-language understanding in computational pathology, providing insights for the design of clinically grounded multimodal pathology model...
This work proposes a novel Asymmetric Dual-text Guided Network (ADGNet), comprising an image-agnostic abstract target prompt and an image-specific detailed background prompt, and introduces an Asymmetric Dual-Branch Interaction module to separately guide visual features with their respective text priors, protecting tar...
Whether attention weights faithfully reflect model reasoning has been actively debated in NLP, yet this question remains largely unexplored for the visual modality in Vision-Language Models (VLMs). We address this gap through causal perturbation analysis on current VLMs, evaluating both the comprehensiveness and suffic...
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Solaris is introduced, an interface world model that instead generates an interactive UI directly, frame by frame, in response to user actions, and enables open-ended interactions that need not be explicitly programmed in advance.
We present Voxel-Grounded Online Instance Manager (VOIM), a training-free voxel-grounded instance manager that builds open-vocabulary 3D instance maps from RGB-D or from monocular RGB alone, a regime no prior training-free system addresses. Online systems typically segment object instances and label them at first detec...
DP-TabImage is proposed, a modality-specialized framework for private paired synthesis that achieves a strong balance among tabular fidelity, image fidelity, and cross-modal alignment and reveals that visual warm-up primarily improves marginal image fidelity, whereas aligned table-image warm-up is critical for improvin...
This work introduces VFStance, which leverages visual framing to make implicit stance cues more explicit via image generation and highlights its potential use beyond automated stance detection.
A single consumer-grade GPU running a vision-language model (VLM) is used to supply missing guidance on pixel-level segmentation, improving segmentation while producing structured, auditable evidence that drives the result and can be inspected on its own.
FoldingAgent is presented, an agentic framework for inferring explicit parametric folding programs directly from origami demonstration videos that leverages the reasoning power of a pre-trained Vision-Language Model equipped with a suite of specialized tools that enable the agent to simulate geometric transitions, veri...
CASTER is introduced, a gradient-free method that stores source class statistics in a discriminative subspace, estimates a class-shared affine transformation from target-batch moments, and analytically transports the source class distributions before classification, which positions CASTER as a lightweight adaptation me...
To improve deployable small VLMs, KPA is proposed, a knowledge-preserving adaptation framework that combines structured perception-to-decision prompting, SLERP-based expert merging, and RegMoE, a regime-aware LoRA mixture-of-experts module that preserves the pretrained model's open-world knowledge while allocating ligh...
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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