GLM-OCR: The Ultimate Guide to the 0.9B Lightweight OCR Powerhouse for Complex Documents
2 days ago 高效码农GLM-OCR: A 0.9B Lightweight Multimodal OCR Model — Complete Guide to Performance, Deployment & Practical Use Abstract: GLM-OCR is a multimodal OCR model with only 0.9B parameters. It achieved a top score of 94.62 on OmniDocBench V1.5, supports deployment via vLLM, SGLang, and Ollama, delivers a PDF parsing throughput of 1.86 pages/second, adapts to complex document scenarios, and balances efficient inference with high-accuracy recognition. Introduction: Why GLM-OCR Stands Out as the Top Choice for Complex Document OCR? If you’re a developer working on document processing or data extraction, you’ve likely faced these pain points: Traditional OCR models struggle with low …
PaddleOCR-VL-1.5: How a 0.9B Model Achieves 94.5% Document Parsing Accuracy
6 days ago 高效码农PaddleOCR-VL-1.5: The 0.9B Parameter Revolution in Document Parsing Core Question: How can a sub-1GB lightweight model achieve 94.5% accuracy in document parsing under real-world complex scenarios? The answer is straightforward: PaddleOCR-VL-1.5 delivers. This vision-language model with only 0.9B parameters achieves 94.5% accuracy on OmniDocBench v1.5, surpassing all previous comparable models. More importantly, this isn’t laboratory performance under ideal conditions—it’s real-world capability across scanning artifacts, skew, warping, screen photography, and illumination variations. My biggest takeaway from testing this model: finally, a model that understands real-world chaos. How many documents we process daily are perfectly scanned and perfectly aligned? Most are phone-captured …
How Gemini 3 Flash’s Agentic Vision Transforms Image Analysis with Code
8 days ago 高效码农Agentic Vision in Gemini 3 Flash: How Visual Reasoning and Code Execution Redefine Image Understanding In the rapidly evolving field of artificial intelligence, particularly within large vision models, we have long faced a fundamental challenge: models typically process the world in a single, static glance. They act like a casual observer scanning a photograph; if they miss a fine-grained detail—such as a serial number on a microchip, a distant street sign, or a specific line in a complex blueprint—they are forced to guess. This “one-shot” processing method often reveals its limitations when faced with tasks requiring extreme precision and complex …
DeepSeek-OCR 2: The AI That Reads Documents Like a Human Using Visual Causal Flow
9 days ago 高效码农DeepSeek-OCR 2: Visual Causal Flow – A New Chapter in Human-Like Visual Understanding Core Question: How can traditional Vision-Language Models (VLMs) break free from rigid raster-scan limitations to achieve document understanding based on “Visual Causal Flow”? In the rapidly evolving landscape of multimodal large models, we have grown accustomed to treating images as static 2D matrices, converting them into 1D token sequences for input into Large Language Models (LLMs). However, does the default “top-left to bottom-right” rigid processing really align with human intuition when reading complex documents? When facing academic PDFs containing formulas, tables, multi-column layouts, or complex logical structures, …
Action100M: A Deep Dive into a Million-Scale Video Action Understanding Dataset
20 days ago 高效码农In the field of artificial intelligence, particularly computer vision and video understanding, high-quality, large-scale datasets are the critical foundation for driving technological progress. Today, we take an in-depth look at a significant resource released by Meta FAIR in collaboration with several top academic institutions—Action100M. This is a project aimed at advancing fine-grained video action understanding through a massive dataset. This article will provide a comprehensive and thorough explanation, from the dataset’s composition and core features to its specific usage. Dataset Overview: Scale and Source Action100M, as the name suggests, targets a scale of one million annotated video segments. Currently, the …
Thinking with Map: How AI Achieves Human-Like Image Geolocation
24 days ago 高效码农Thinking with Map: How AI Learned to “Think” Like Humans Using Maps for Precise Image Geolocalization ### Quick Summary (Featured Snippet Ready) Thinking with Map is an advanced agentic framework that enables large vision-language models (LVLM) to perform image geolocalization by actively querying maps — just like humans do. Built on Qwen3-VL-30B-A3B, it combines reinforcement learning and parallel test-time scaling to dramatically boost accuracy. On the new MAPBench (China-focused, up-to-date street-view benchmark), it achieves 44.98% Acc@500m on easy cases and 14.86% on hard cases — significantly outperforming Gemini-3-Pro with Google Search/Map (20.86% → 4.02% on the same splits) and other …
VideoRAG: How Machines Finally Crack Extreme Long-Context Video Understanding
25 days ago 高效码农VideoRAG & Vimo: Cracking the Code of Extreme Long-Context Video Understanding Core Question: Why do existing video AI models fail when faced with hundreds of hours of footage, and how does the VideoRAG framework finally enable machines to chat with videos of any length? When we first attempted to analyze a 50-hour university lecture series on AI development, our state-of-the-art video model choked after the first three hours. It was like trying to understand an entire library by reading random pages from three books. That’s when we realized the fundamental flaw: current video understanding approaches treat long videos as isolated …
UniVideo Explained: The Single Open-Source Model That Understands, Generates & Edits Videos with AI
26 days ago 高效码农UniVideo in Plain English: One Model That Understands, Generates, and Edits Videos Core question: Can a single open-source model both “see” and “remix” videos without task-specific add-ons? Short answer: Yes—UniVideo freezes a vision-language model for understanding, bolts a lightweight connector to a video diffusion transformer, and trains only the connector + diffusion net; one checkpoint runs text-to-video, image-to-video, face-swap, object removal, style transfer, multi-ID generation, and more. What problem is this article solving? Reader query: “I’m tired of chaining CLIP + Stable-Diffusion + ControlNet + RVM just to edit a clip. Is there a unified pipeline that does it all, …
Counterfactual Video Generation: A Breakthrough to Reduce Hallucinations in Multimodal AI
1 months ago 高效码农Reducing Hallucinations in Multimodal Large Language Models for Video Understanding Through Counterfactual Video Generation Have you ever wondered why multimodal large language models sometimes give answers that sound logical but don’t match what’s actually happening in a video? For instance, if a video shows an object suddenly vanishing, the model might insist it’s still there, relying more on everyday common sense than on the visual evidence right in front of it. This is known as “visual ungrounded hallucinations.” In this article, we’ll explore a innovative approach that uses specially generated counterfactual videos to help these models better understand videos and …
Degradation-Aware Reasoning: Experience Robust-R1’s Visual Understanding Demo
1 months ago 高效码农Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding – A Deep Dive into the AAAI 2026 Oral Presentation In the field of computer vision, robustness has long been a core concern for researchers and developers alike. In real-world applications, images and videos are frequently affected by various degradation factors—such as blur, noise, lighting variations, and compression artifacts—all of which can significantly impair a model’s ability to understand visual content. Today, we’re exploring Robust-R1, a groundbreaking solution designed to address this critical challenge. As an oral presentation highlight at AAAI 2026, Robust-R1 centers on “degradation-aware reasoning,” offering a fresh perspective on achieving …
How WorldWarp’s Async Video Diffusion Creates 1000-Frame 3D Scenes from One Photo
1 months ago 高效码农From One Photo to a 200-Frame Walk-Through: How WorldWarp’s Async Video Diffusion Keeps 3D Scenes Stable A plain-language, code-included tour of the open-source WorldWarp pipeline For junior-college-level readers who want stable, long-range novel-view video without the hype 1. The Problem in One Sentence If you give a generative model a single holiday snap and ask it to “keep walking forward”, most pipelines either: lose track of the camera, or smear new areas into a blurry mess. WorldWarp (arXiv 2512.19678) fixes both problems by marrying a live 3D map with an async, block-by-block diffusion model. The code is public, the weights …
Pixel-Semantic VAE: The AI Breakout Uniting Image Understanding and Creation
1 months ago 高效码农Both Semantics and Reconstruction Matter: Making Visual Encoders Ready for Text-to-Image Generation and Editing Why do state-of-the-art vision understanding models struggle with creative tasks like image generation? The answer lies in a fundamental disconnect between recognition and reconstruction. Imagine asking a world-renowned art critic to paint a portrait. They could eloquently dissect the composition, color theory, and emotional impact of any masterpiece, but if handed a brush, their actual painting might be awkward and lack detail. A similar paradox exists in artificial intelligence today. Modern visual understanding systems—powered by representation encoders like DINOv2 and SigLIP—have become foundational to computer vision. …
How Qwen-Image-Layered Solves AI’s Biggest Image Editing Problem with Layer Decomposition
1 months ago 高效码农Qwen-Image-Layered: A Deep Dive into AI’s Solution for Consistent Image Editing via Layer Decomposition The world of AI-generated imagery has exploded in recent years. Models can now create stunningly realistic photos, imaginative art, and complex scenes from simple text prompts. However, a significant challenge has persisted beneath this surface of impressive synthesis: editing these images with precision and consistency. Have you ever tried to change the color of a car in an AI-generated image, only to find that the background windows or the person standing next to it also warp and distort? This frustrating phenomenon, where edits in one area …
How LongVie 2 Solves AI Video Generation: Sharp, Steerable 5-Minute Clips
1 months ago 高效码农LongVie 2 in Plain English: How to Keep AI-Generated Videos Sharp, Steerable, and Five-Minutes Long “ Short answer: LongVie 2 stacks three training tricks—multi-modal control, first-frame degradation, and history context—on top of a 14 B diffusion backbone so you can autoregressively create 3–5 minute clips that stay visually crisp and obey your depth maps and point tracks the whole way through. What problem is this article solving? “Why do today’s video models look great for 10 seconds, then turn into blurry, flickering soup?” Below we walk through LongVie 2’s pipeline, show exact commands to run it on a single A100, …
Scone AI: The Breakthrough in Precise Subject-Driven Image Generation
1 months ago 高效码农Scone: Teaching AI to “Pick the Right Person” in a Crowd – A Leap Towards Precise Subject-Driven Image Generation Snippet The Scone model addresses a critical challenge in subject-driven image generation: accurately identifying and generating only the instruction-specified subject from a reference image containing multiple candidates. It introduces an “understanding bridge strategy” within a unified understanding-generation architecture, leveraging the early semantic advantages of the understanding expert to guide the generation process. This results in superior composition and distinction capabilities, achieving a leading overall score of 8.50 among open-source models on the new SconeEval benchmark. Have you ever imagined handing an …
PersonaLive: The Real-Time Portrait Animation Breakthrough Changing Live Streaming
1 months ago 高效码农PersonaLive: A Breakthrough Framework for Real-Time Streaming Portrait Animation Abstract PersonaLive is a diffusion model-based portrait animation framework that enables real-time, streamable, infinite-length portrait animations on a single 12GB GPU. It balances low latency with high quality, supporting both offline and online inference, and delivers efficient, visually stunning results through innovative technical designs. What is PersonaLive? In today’s booming short-video social media landscape, live streamers and content creators have an urgent demand for high-quality portrait animation technology. Enter PersonaLive—a groundbreaking framework developed collaboratively by the University of Macau, Dzine.ai, and the GVC Lab at Great Bay University. Simply put, PersonaLive …
InfinityStar: Revolutionizing Video Generation with Unified Spacetime Autoregressive Modeling
1 months ago 高效码农InfinityStar: Unified Spacetime Autoregressive Modeling for Visual Generation Introduction: What is InfinityStar and How Does It Address Challenges in Visual Generation? This article aims to answer the core question: What is InfinityStar, how does it unify image and video generation tasks, and why does it improve efficiency and quality? InfinityStar is a unified spacetime autoregressive framework designed for high-resolution image and dynamic video synthesis. It leverages recent advances in autoregressive modeling from both vision and language domains, using a purely discrete approach to jointly capture spatial and temporal dependencies in a single architecture. Visual synthesis has seen remarkable advancements in …
OneStory: How Adaptive Memory Solves Multi-Shot Video Generation’s Biggest Challenge
1 months ago 高效码农OneStory: Redefining Multi-Shot Video Generation with Adaptive Memory Abstract OneStory addresses the critical challenge of maintaining narrative coherence across discontinuous video shots by introducing an adaptive memory system. This framework achieves a 58.74% improvement in character consistency and supports minute-scale video generation through next-shot prediction and dynamic context compression. By reformulating multi-shot generation as an autoregressive task, it bridges the gap between single-scene video models and complex storytelling requirements. What is Multi-Shot Video Generation? Imagine watching a movie where scenes seamlessly transition between different locations and characters. Traditional AI video generators struggle with this “multi-shot” structure—sequences of non-contiguous clips that …
LivingSwap: The Breakthrough in Cinematic Video Face Swapping Using Source Video Reference
1 months ago 高效码农Title: High-Fidelity Face Swapping for Cinematic Quality: When AI Learns to “Reference” the Source Video Snippet: LivingSwap is the first video face-swapping model to use the source video itself as a pixel-level reference. By combining keyframe-guided identity injection with a novel reference-guided generation architecture, it achieves unprecedented temporal consistency and attribute fidelity in long, complex video sequences, reducing manual editing effort by up to 40x for film production. Imagine this scenario: an actor becomes unavailable to complete filming, or a director wants to recast a role in post-production. Traditionally, this meant costly reshoots or painstaking, frame-by-frame manual editing prone to …
PaCo-RL: How This Breakthrough Solves AI Image Consistency with Reinforcement Learning
1 months ago 高效码农PaCo-RL: A Breakthrough in Consistent Image Generation Using Reinforcement Learning Introduction Have you ever tried using AI to generate a series of coherent images—for creating story characters or designing multiple advertisement visuals—only to find the results inconsistent in style, identity, or logical flow? Consistent image generation remains a fundamental challenge in AI content creation, requiring models to maintain shared elements like character appearance, artistic style, or scene continuity across multiple images. In this comprehensive guide, we explore PaCo-RL (Pairwise Consistency Reinforcement Learning), an innovative framework that addresses these challenges through specialized reward modeling and efficient reinforcement learning. Whether you’re a …