Building smarter maps with GPT-4o vision fine-tuning

Introducing vision to the fine-tuning API

Developers can now fine-tune GPT-4o with images and text to improve vision capabilities

Fine-tuning now available for GPT-4o

GPT-4o mini: advancing cost-efficient intelligence

Introducing 4o Image Generation

At OpenAI, we have long believed image generation should be a primary capability of our language models. That’s why we’ve built our most advanced image generator yet into GPT‑4o.

NavOne: One-Step Global Planning for Vision-Language Navigation on Top-Down Maps

arXiv:2605. 06317v4 Announce Type: replace-cross Abstract: Existing Vision-Language Navigation (VLN) methods typically adopt an egocentric, step-by-step paradigm, which struggles with error accumulation and limits efficiency.

Transforming visual accessibility

Be My Eyes uses GPT-4 to transform visual accessibility.

MapLightning: Online Vectorized HD Map Construction with 1D Map Tokens

arXiv:2610.01905v1 Announce Type: new Abstract: Online vectorized HD map construction is essential for scaling safe autonomous driving and requires accurate, real-time inference. Prior methods typica...

New chip could help tiny robots traverse complex environments

Researchers combined an efficient algorithm with dedicated hardware to rapidly generate 3D maps for navigation using minimal memory and power.

Fine-tuning GPT-3 to scale video creation

Fine-tuning GPT-3 to power and scale done-for-you video creation.

Addendum to GPT-4o System Card: 4o image generation

4o image generation is a new, significantly more capable image generation approach than our earlier DALL·E 3 series of models. It can create photorealistic output.

DSP-SLAM++: A Unified Framework for Multi-Class, High-Fidelity Object SLAM in the Wild

Existing object-aware SLAM systems force a trade-off between real-time performance, multi-class support, and the generation of high-fidelity, semantically coherent object models. To address this trade-off, we present DSP-SLAM++, which extends the DSP-SLAM framework with an asynchronous mapping pipeline for real-time performance and dedicated sensor fusion adaptations for a monocular fisheye-LiDAR suite.

Fusion of Pervasive RF Data with Spatial Images via Vision Transformers for Enhanced Mapping in Smart Cities

arXiv:2508. 03736v2 Announce Type: replace-cross Abstract: In this paper, we present a deep learning-based approach that integrates the DINOv2 architecture to improve building mapping by combining (possibly erroneous) maps from open-source platforms with pervasive radio frequency (RF) data collected from multiple wireless user equipments and base stations.