Official Amap technology account showcasing all of Amap's technical innovations.
This article explores how Gaode leverages AI and large‑scale spatio‑temporal pre‑training to transform uncertain urban transit into reliable, real‑time bus and subway predictions, detailing technical challenges, model architectures, and user‑focused features that enhance daily commuting.
Omni-Effects introduces a unified framework that combines LoRA‑based expert mixture models and spatially aware prompts to generate multiple, precisely placed visual effects in video, supported by the new Omni‑VFX dataset and evaluation suite, demonstrating superior spatial control and diversity over prior single‑effect methods.
This article describes how the Gaode terminal team tackled large‑scale repository upgrades by building a code‑RAG and code‑Agent tool, addressing recall and stability issues, then fine‑tuning a small LLM (Qwen3‑4B) with LoRA and custom datasets to achieve reliable, low‑cost, on‑device code‑query performance.
The paper introduces FingER, an entity-level evaluation framework and the FingER-Instruct-60k dataset for assessing AI-generated video quality with fine-grained reasoning, and demonstrates state-of-the-art zero-shot performance on multiple benchmarks using novel training strategies.
This article introduces FingER, a novel entity‑level evaluation framework and the FingER‑Instruct‑60k dataset for assessing AI‑generated video quality with fine‑grained reasoning, and demonstrates its state‑of‑the‑art performance on multiple benchmarks using advanced training strategies such as GRPO.
The UPRE framework introduces multi‑view domain prompts and unified representation enhancement to achieve zero‑shot domain adaptation for object detection, dramatically improving detection accuracy on unseen target domains across diverse visual scenarios.
The UPRE paper, presented at ICCV, introduces a multi‑view domain prompt and a unified representation enhancement to enable zero‑shot domain adaptation for object detection, achieving state‑of‑the‑art performance across diverse weather, geographic, and synthetic‑to‑real scenarios.
The USP framework introduces masked latent modeling within a VAE space to pre‑train ViT encoders, enabling seamless weight transfer to both image classification, segmentation, and diffusion‑based generation tasks, dramatically speeding up DiT and SiT models while preserving strong visual representations.
The USP framework introduces masked latent modeling within a VAE space to pretrain ViT encoders, enabling seamless weight transfer to both image classification and diffusion‑based generation tasks, dramatically accelerating training while preserving strong performance across multiple benchmarks.
This article introduces VMBench, the first perception‑aligned video motion generation benchmark that defines a five‑dimensional metric suite and a meta‑guided prompt generation pipeline, and presents LD‑RPS, a zero‑shot unified image restoration framework based on latent diffusion recurrent posterior sampling, together with extensive experiments validating both systems.
