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vision-language model

29 articles · Page 1 of 1
Sohu Tech Products
Sohu Tech Products
Aug 26, 2026 · Artificial Intelligence

DeepSeek-V4-Flash-Vision-Exp Multimodal Evaluation: Strong Recognition but Over-Inference on Real-World Context

The author evaluates DeepSeek's new multimodal model across four visual reasoning challenges, finding excellent recognition and structured reasoning capabilities but a consistent tendency to hallucinate real-world details not present in images, a common limitation in vision-language models.

DeepSeekHallucinationOCR
0 likes · 9 min read
DeepSeek-V4-Flash-Vision-Exp Multimodal Evaluation: Strong Recognition but Over-Inference on Real-World Context
PaperAgent
PaperAgent
Aug 22, 2026 · Artificial Intelligence

DeepSeek’s Hidden Multimodal Model: Technical Deep‑Dive and Unexpected Bugs

The article reviews DeepSeek‑V4‑Flash‑Vision‑Exp, exposing a misidentification bug, detailing its visual‑primitive approach, impressive spatial‑reasoning benchmarks, and a highly compressed KV‑cache architecture that balances performance with efficiency.

DeepSeekKV cache compressionMoE
0 likes · 4 min read
DeepSeek’s Hidden Multimodal Model: Technical Deep‑Dive and Unexpected Bugs
Machine Heart
Machine Heart
Jul 24, 2026 · Artificial Intelligence

From One‑Shot Answers to Action Chains: S‑Agent Advances Spatial Intelligence

S‑Agent redefines spatial intelligence by replacing single‑shot answers with a multi‑step action chain that combines a vision‑language model for task planning, specialized depth and pose models for 3D alignment, and a spatial expert that converts geometry into usable evidence, achieving state‑of‑the‑art zero‑shot scores on MMSI‑Bench and ViewSpatial‑Bench and further improvements after distilling 29.2 k trajectories into an 8‑billion‑parameter model.

Agent Architecturemodel distillationspatial intelligence
0 likes · 11 min read
From One‑Shot Answers to Action Chains: S‑Agent Advances Spatial Intelligence
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jul 24, 2026 · Artificial Intelligence

When Large Models Begin to “Understand” Anomalies: The New Wave in Video Anomaly Detection

The article traces video anomaly detection from simple alarm‑style scores to semantic, open‑vocabulary understanding powered by vision‑language and large language models, highlighting recent CVPR 2024 work, emerging capabilities, and future evaluation criteria beyond raw detection accuracy.

LAVADLarge Language Modelopen‑vocabulary VAD
0 likes · 6 min read
When Large Models Begin to “Understand” Anomalies: The New Wave in Video Anomaly Detection
Machine Heart
Machine Heart
Jul 23, 2026 · Artificial Intelligence

EgoSteer: System for Dual Dexterous Hands Using Human Data and World Models

EgoSteer is an open‑source full‑stack framework that transforms massive first‑person human‑hand videos into high‑quality training data via the EgoSmith pipeline, trains a flow‑matching VLA augmented with a lightweight world‑model expert, and integrates a unified action space and real‑time chunking to enable dual dexterous hands to follow natural language commands, achieving up to 75 % success across 40 diverse manipulation tasks.

EgoSteerFew-Shot Learningdata pipeline
0 likes · 17 min read
EgoSteer: System for Dual Dexterous Hands Using Human Data and World Models
Machine Heart
Machine Heart
Jul 15, 2026 · Artificial Intelligence

Tencent Releases Two Embodied AI Models—Hy‑Embodied‑VLM‑1.0 & RxBrain‑1.0—to Boost Robot Real‑World Understanding

Tencent's Robotics X and Hunyuan teams open‑source two embodied AI foundation models—Hy‑Embodied‑VLM‑1.0 and Hy‑Embodied‑RxBrain‑1.0—detailing their layered perception‑action‑adaptation design, massive multimodal training data, benchmark superiority over competing models, and real‑robot validation showing high success rates across complex tasks.

Embodied AIbenchmarkmultimodal models
0 likes · 14 min read
Tencent Releases Two Embodied AI Models—Hy‑Embodied‑VLM‑1.0 & RxBrain‑1.0—to Boost Robot Real‑World Understanding
JD Cloud Developers
JD Cloud Developers
Jun 23, 2026 · Artificial Intelligence

From Q&A to Real‑Time Seeing & Speaking: JD’s First Open‑Source JoyAI‑VL‑Interaction

JD’s open‑source JoyAI‑VL‑Interaction transforms large‑model AI from static question‑answering to continuous, on‑scene observation, proactive judgment, and real‑time response, offering agent delegation and achieving up to 87.9% win rate against leading video assistants in live benchmarks.

AI assistantbenchmarkmultimodal AI
0 likes · 9 min read
From Q&A to Real‑Time Seeing & Speaking: JD’s First Open‑Source JoyAI‑VL‑Interaction
Machine Heart
Machine Heart
Jun 9, 2026 · Artificial Intelligence

Why Standard Vision‑Language Models + Scale Data Beat Specialized 3D Vision Designs (VLM³)

Meta’s VLM³ demonstrates that a plain vision‑language model, when trained on large‑scale data with simple camera‑focal‑length and pixel‑space normalization, matches or surpasses expert 3D vision models across monocular depth estimation, object‑level understanding, pixel‑matching and camera‑pose tasks, eliminating the need for task‑specific architectures, loss functions, data augmentations or regression formulations.

3D visionDepth EstimationMeta
0 likes · 6 min read
Why Standard Vision‑Language Models + Scale Data Beat Specialized 3D Vision Designs (VLM³)
Machine Heart
Machine Heart
Jun 9, 2026 · Artificial Intelligence

How PhysForge Generates Interactive 3D Assets from a Single Image

PhysForge, a physics‑grounded 3D asset generation framework accepted at ICML 2026, converts a single input image into a fully interactive 3D object by first planning a hierarchical physical blueprint with a vision‑language model and then refining geometry, texture, and precise kinematic parameters via a diffusion model, supported by the large‑scale PhysDB dataset.

3D GenerationLarge DatasetRobotics Simulation
0 likes · 10 min read
How PhysForge Generates Interactive 3D Assets from a Single Image
HyperAI Super Neural
HyperAI Super Neural
Mar 12, 2026 · Artificial Intelligence

Stanford’s Merlin: Single‑GPU 3D Abdominal CT Vision‑Language Model Leads 752 Tasks

Stanford researchers introduced Merlin, the first native 3D abdominal CT vision‑language foundation model trained on a single NVIDIA A6000 GPU using a 25,494‑scan dataset, and demonstrated its superiority across 752 benchmark tasks—including zero‑shot classification, phenotype prediction, cross‑modal retrieval, disease forecasting, report generation, and 3D segmentation—outperforming existing baselines.

3D CTDisease Predictionmedical imaging AI
0 likes · 18 min read
Stanford’s Merlin: Single‑GPU 3D Abdominal CT Vision‑Language Model Leads 752 Tasks
Old Zhang's AI Learning
Old Zhang's AI Learning
Mar 10, 2026 · Artificial Intelligence

FireRed-OCR 2B: An Open‑Source VLM That Tackles Structural Hallucination

FireRed‑OCR‑2B, an open‑source 2‑billion‑parameter visual‑language model, addresses structural hallucination in document OCR through a geometry‑aware data factory and a three‑stage training pipeline, achieving a 92.94 OmniDocBench v1.5 score and leading end‑to‑end performance while remaining lightweight enough for consumer‑grade GPUs.

FireRed-OCROCROmniDocBench
0 likes · 11 min read
FireRed-OCR 2B: An Open‑Source VLM That Tackles Structural Hallucination
HyperAI Super Neural
HyperAI Super Neural
Dec 6, 2025 · Artificial Intelligence

Quick Look at This Week’s Frontier AI Papers: DeepSeekMath‑V2, MedSAM‑3, SAM 3D, Qwen3‑VL, and M²

This roundup surveys five cutting‑edge AI papers—DeepSeekMath‑V2’s self‑verifiable mathematical reasoning, MedSAM‑3’s promptable medical image and video segmentation, SAM 3D’s single‑image 3D reconstruction, Qwen3‑VL’s high‑capacity vision‑language model, and the M² memory‑mesh transformer for image captioning—highlighting their key methods, benchmarks, and code links.

3D reconstructionImage Captioninglarge language models
0 likes · 6 min read
Quick Look at This Week’s Frontier AI Papers: DeepSeekMath‑V2, MedSAM‑3, SAM 3D, Qwen3‑VL, and M²
AI Algorithm Path
AI Algorithm Path
Dec 1, 2025 · Artificial Intelligence

Getting Started with the Cutting‑Edge Vision‑Language Model Qwen3‑VL

This article introduces vision‑language models, explains why they outperform OCR‑plus‑LLM pipelines, and walks through practical OCR and information‑extraction tasks using Qwen3‑VL, complete with code snippets, example prompts, result analysis, and a discussion of the model's limitations and resource considerations.

Deep LearningOCRPython
0 likes · 13 min read
Getting Started with the Cutting‑Edge Vision‑Language Model Qwen3‑VL
Fun with Large Models
Fun with Large Models
Oct 26, 2025 · Artificial Intelligence

From Deep Learning to Large‑Model OCR: Which Model Leads the Pack?

This article traces OCR's evolution from early CNN‑LSTM systems to modern multimodal VLMs, analyzes leading open‑source models such as DeepSeek‑OCR, PaddleOCR, and MonkeyOCR, and offers practical guidance for long‑document, academic, and edge‑computing scenarios.

DeepSeek-OCRMonkeyOCROCR
0 likes · 15 min read
From Deep Learning to Large‑Model OCR: Which Model Leads the Pack?
DataFunTalk
DataFunTalk
Oct 20, 2025 · Artificial Intelligence

How DeepSeek-OCR Achieves 10× Context Compression with Vision Tokens

DeepSeek-OCR, a newly open‑sourced 3B‑parameter OCR model, uses a novel DeepEncoder and a 3B MoE decoder to compress long‑text contexts into visual tokens, achieving up to 10× compression with 97% accuracy and demonstrating strong practical performance on benchmarks and multilingual documents.

Context CompressionDeepSeekOCR
0 likes · 11 min read
How DeepSeek-OCR Achieves 10× Context Compression with Vision Tokens
Amap Tech
Amap Tech
Oct 3, 2025 · Artificial Intelligence

How OmniNav Unifies Multi‑Task Embodied Navigation with a Fast‑Slow Dual System

OmniNav introduces a unified framework for embodied navigation that simultaneously handles instruction‑goal, object‑goal, point‑goal, and frontier‑based exploration tasks using a fast visual‑language‑driven policy and a slow memory‑augmented planner, achieving state‑of‑the‑art performance and real‑world 5 Hz deployment.

Multimodal Trainingcontinuous controlembodied navigation
0 likes · 9 min read
How OmniNav Unifies Multi‑Task Embodied Navigation with a Fast‑Slow Dual System
DataFunTalk
DataFunTalk
Sep 7, 2025 · Artificial Intelligence

Why Apple’s FastVLM Is 85× Faster and What It Means for On‑Device AI

Apple recently open‑sourced its FastVLM and MobileCLIP2 models, showcasing a multimodal vision‑language system that runs up to 85 times faster than comparable models, enabling real‑time AI on iPhones and other edge devices while illustrating Apple’s broader “B‑plan” of on‑device small‑model AI strategy.

AppleFastVLMmultimodal
0 likes · 15 min read
Why Apple’s FastVLM Is 85× Faster and What It Means for On‑Device AI
AI Algorithm Path
AI Algorithm Path
Jul 15, 2025 · Artificial Intelligence

Day 8: Fine‑Tuning CLIP for Image‑Text Tasks – A Beginner’s Guide

This tutorial walks through fine‑tuning OpenAI's CLIP ViT‑B/32 on a small image‑text dataset in a Kaggle notebook, covering environment setup, model loading, data preprocessing with CLIPProcessor, training a linear head, and observing loss convergence to align visual and textual embeddings.

CLIPKagglePyTorch
0 likes · 5 min read
Day 8: Fine‑Tuning CLIP for Image‑Text Tasks – A Beginner’s Guide
AIWalker
AIWalker
May 29, 2025 · Artificial Intelligence

ImgEdit-Bench Exposes Weak Image Editing Models – A ‘Death Test’ Reveals Who’s Struggling

ImgEdit introduces a large‑scale, high‑quality editing dataset and the ImgEdit‑Bench benchmark, detailing a robust data‑generation pipeline, multi‑round editing tasks, and a specialized evaluation model, and demonstrates through extensive experiments that its ImgEdit‑E1 model outperforms existing open‑source editors and narrows the gap with closed‑source systems.

AIImage Editingbenchmark
0 likes · 20 min read
ImgEdit-Bench Exposes Weak Image Editing Models – A ‘Death Test’ Reveals Who’s Struggling
AI Algorithm Path
AI Algorithm Path
Apr 2, 2025 · Artificial Intelligence

Vision‑Reasoning Model: Enabling LLMs to See and Think

The article analyzes the limitations of current visual language models and large reasoning models, proposes a combined Vision‑Reasoning Model (VRM), details its architecture using LLaVA, describes end‑to‑end fine‑tuning and reinforcement‑learning reward design, and argues that such models will become the next breakthrough in AI.

DeepSeekLLaVALarge Language Model
0 likes · 9 min read
Vision‑Reasoning Model: Enabling LLMs to See and Think
JavaEdge
JavaEdge
Mar 27, 2025 · Artificial Intelligence

Can a Single LLM Both See and Reason? Exploring Visual Reasoning Models (VRM)

This article examines the limitations of current vision‑language and reasoning models, proposes a visual reasoning model (VRM) that can process images and perform deep logical inference, and discusses architecture, training methods, reinforcement‑learning reward designs, and practical challenges.

Artificial IntelligenceDeep LearningLLM
0 likes · 8 min read
Can a Single LLM Both See and Reason? Exploring Visual Reasoning Models (VRM)
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 20, 2025 · Artificial Intelligence

How LLMs Power Real-Time Interactive 3D Worlds in Unreal Engine

This article explains how large language models are integrated with Unreal Engine to enable natural‑language‑driven 3D model search, manipulation, and scene understanding, detailing metadata extraction, vision‑language labeling, RAG‑based retrieval, and function‑call translation for interactive virtual environments.

3D interactionLLMRAG
0 likes · 21 min read
How LLMs Power Real-Time Interactive 3D Worlds in Unreal Engine