arXiv
Verdict: NotableWeVisOne-VL: Unified Visual Perception from Objects to Pixels
Existing vision-language models (VLMs) remain unreliable for fine-grained visual perception.
- HF ▲ 241
- Stars ★ 143
Models, methods, and the machinery of intelligence.
arXiv
Verdict: NotableExisting vision-language models (VLMs) remain unreliable for fine-grained visual perception.
Retrieval Augmented Generation (RAG) grounds large language models in external knowledge.
As LLMs expand their language coverage, evaluating how well they understand and represent diverse cultures becomes increasingly important.
Using an LLM to generate training data for another is among the most general ways to train one, and it remains common practice.
Agent harnesses have become a central component of modern agentic systems, shaping how models use tools, manage context, and coordinate long-horizon interaction.
Diffusion Language Models (DLMs) introduce unique quantization challenges due to their iterative denoising process.
Vision-language models (VLMs) can perform a broad range of tasks across diverse settings, yet their performance in agentic contexts remains poorly understood.
AI coding agents can now build small playable games in minutes, but the results usually look like programmer art: gray blockouts, colored geometric primitives, and flat semantic blobs.
This paper studies category-level RGB-D object pose estimation, which recovers an object's 3D rotation and translation without instance-specific CAD models or reference views.