AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,462 papers
Recent advances in text-to-video (T2V) diffusion models have demonstrated remarkable generative capabilities, yet their reliance on loosely curated training data raises pressing safety and copyright concerns. Concept erasure offers a principled remedy by removing unwanted semantics from pretrained models while preservi...
It is found that synthetic-only training can be competitive, and the 0.9B-parameter PaddleOCR-VL-1.6 is adapted into Wayu-Paxa-OCR-Zero, a Thai OCR model adapted without OCR labels from real Thai document pages, showing that synthetic-only training can be competitive.
Extensive experiments on the M-BEIR benchmark demonstrate that WIDE outperforms state-of-the-art generative retrieval methods, effectively suppressing forced hallucination while maintaining compact index structures.
This paper proposes a method to address gradient vanishing with a piecewise truncated gradient formulation that improves the optimization stability and robustness to initializations and introduces a novel dataset for benchmarking dynamic Gaussian Splatting using synthetic 3D scenes.
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A method combining deformable temporal alignment and difference-aware spatial selective fusion is proposed, used to generate complementary temporal context and achieves certain rate-distortion performance improvement over DCVC-DC.
The resulting map, released as an open-access product, is the first high-resolution national tree species map of Denmark and provides a valuable resource for forest monitoring, ecological research, and land management applications.
OmniRSCLIP is an end-to-end contrastive learning framework that supports multi-source sensor inputs for remote sensing vision-language modeling and introduces a spectral-context-aware mask-based contrastive learning scheme to suppress modality-specific redundant features and enhance fine-grained image-text alignment.
This work introduces a human-aligned evaluation framework for text-to-SVG generation, and develops two complementary evaluators: CLIP scorers adapted to vector graphics and then aligned to human preferences, for fast large-scale evaluation and a VLM judge trained with supervised fine-tuning and reward-shaped reinforcem...
This work proposes SciFigQual-Bench, a full-text contextual benchmark that evaluates scientific image across five dimensions (clarity, layout,captionfit, contextrelevance, andmisleadingrisk), and develops SFQ-Agent to achieve auditable and refined scoring through the collection and fusion of modal evidence.
Experiments on NSFW, artistic style, and object erasure, with a large-scale robustness-utility analysis over many CET baselines, show that PARSE erases multiple concepts robustly without sacrificing post-edit utility.
This work proposes Visual Contrastive Self-Distillation, namely VCSD, which converts image-content removal into an on-policy self-distillation signal, and consistently outperforms matched OPSD across Qwen3-VL and Qwen3.5 models.
A multi-source Masked Autoencoder (MAE) framework, named ProsMAE, for histopathology representation learning, which achieved a higher mean validation quadratic weighted kappa (QWK) than the vanilla MAE frozen linear-probe baseline under the evaluated disjoint PANDA split.
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.
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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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