arXiv AI
WorldSR: Harnessing World Knowledge Search for Grounded Image Super-Resolution
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arXiv Machine Learning
Aug 26
NAIMA: Semantics Aware RGB Guided Depth Super-Resolution
The paper introduces NAIMA, a guided depth super‑resolution framework that leverages global contextual semantic priors from pretrained vision transformer token embeddings. Its Guided Token Attention (GTA) module uses depth encodings as queries to attend over semantic tokens, with a zero‑initialized gate controlling the influence of semantic evidence. NAIMA achieves competitive in‑distribution performance while delivering superior cross‑dataset generalization without relying on decoded priors or auxiliary objectives.
arXiv AI
Oct 15
OpenDepth: Repurposing Image Editing Priors for Robust Open-World Monocular Depth Estimation
arXiv:2610.17610v2 Announce Type: new Abstract: Recently, the integration of generative priors has revolutionized Monocular Depth Estimation (MDE), achieving state-of-the-art (SOTA) performance with...
arXiv Computer Vision
Oct 12
WorldGenBench: A World-Knowledge-Integrated Benchmark for Reasoning-Driven Text-to-Image Generation
WorldGenBench is a benchmark that tests text‑to‑image models on their ability to combine factual grounding with contextual reasoning. It introduces the Knowledge Checklist Score to evaluate whether generated images reflect both explicit and implicit semantic elements. The study finds that while some proprietary models perform well, most open‑source systems struggle, especially with culturally diverse and scientific content, revealing common failure modes such as semantic hallucination and underrepresentation of non‑Western knowledge.
arXiv AI
Jun 26
Semantic Generative Tuning for Unified Multimodal Models
arXiv:2605. 18714v2 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture.
arXiv Computer Vision
1d ago
GeoCore-9B: Towards Geo-Aware Generative Foundation Models in Earth Observation
arXiv:2608.01896v2 Announce Type: replace Abstract: Existing generative models for earth observation (EO) predominantly rely on fine-tuning natural image priors, which limits their scalability and in...
arXiv Computer Vision
Aug 31
Video Generative Models as Geometry Learner
The paper introduces GeoNeXt, a framework that repurposes pretrained video generative models for geometry estimation by framing it as a next‑frame prediction task. Unlike prior methods that either train separate depth/normal models or fine‑tune image diffusion backbones, GeoNeXt jointly models images and geometric targets, leveraging the structured knowledge of video models for more data‑efficient learning. Experiments show zero‑shot monocular depth and surface normal estimation that outperforms existing generative approaches and rivals discriminative state‑of‑the‑art methods while using far less training data.