Vision Models

Extracted Attributes

• Impact

  Drives re-industrialization, contributes to 'New Collar Boom', addresses skilled talent shortage

• Category

  Multimodal Foundation Model

• Mechanism

  Fine-tuning Vision-Language Models (VLMs) on datasets pairing visual observations and language instructions with robot trajectories

• Subcategory

  Vision-Language-Action (VLA) Model

• Applications

  Robot learning, End-to-end manipulation tasks, AI-powered factories, Predictive maintenance, Object detection, Crop monitoring, Visual Question Answering (VQA), Image captioning, Text-to-Image search

• Core Function

  Integrates vision, language, and actions for robot task execution

• Key Components

  Vision-language encoder (VLM or vision transformer), Action decoder

• Related Fields

  Computer Vision, Natural Language Processing (NLP), Robotics

• Developer of RT-2

  Google DeepMind

• Pioneering VLA Model

  RT-2

Timeline

• LeNet, an early convolutional neural network, was designed by Yann LeCun, marking a foundational breakthrough in computer vision models. (Source: web_search_results)

  1998-01-01

• Google DeepMind pioneered the concept of Vision-Language-Action (VLA) models with the release of RT-2, a VLM adapted for end-to-end manipulation tasks. (Source: wikipedia)

  2023-07-01

• OpenAI's GPT-4 with Vision, a multimodal language model with advanced vision capabilities, was released. (Source: web_search_results)

  2023-10-24

Vision-language-action model

In robot learning, a vision-language-action model (VLA) is a class of multimodal foundation models that integrates vision, language and actions. Given an input image (or video) of the robot's surroundings and a text instruction, a VLA directly outputs low-level robot actions that can be executed to accomplish the requested task. VLAs are generally constructed by fine-tuning a vision-language model (VLM, i.e. a large language model extended with vision capabilities) on a large-scale dataset that pairs visual observation and language instructions with robot trajectories. These models combine a vision-language encoder (typically a VLM or a vision transformer), which translates an image observation and a natural language description into a distribution within a latent space, with an action decoder that transforms this representation into continuous output actions, directly executable on the robot. The concept was pioneered in July 2023 by Google DeepMind with RT-2, a VLM adapted for end-to-end manipulation tasks, capable of unifying perception, reasoning and control.

Web Search Results

• Explore Top Computer Vision Models| A Comprehensive Guide

  Several computer vision models exist, including feature-based models, deep learning networks, and convolutional neural networks. These models can learn and recognize patterns and features in the visual environment. They can be trained on abundant quantities of labeled and image data. This article presents a detailed summary of computer vision models so that you acquire an in-depth comprehension of them. Deep Learning service Deep Learning service [...] Explore Top Computer Vision Models | A Comprehensive Guide # Explore Top Computer Vision Models | A Comprehensive Guide Computer vision is an artificial intelligence branch that empowers computers to comprehend and interpret the visual world. It entails deploying algorithms and machine learning models to scrutinize and interpret visual data from various sources, including cameras. [...] Crop Monitoring and Health Assessment: Computer vision models analyze images captured by drones, satellites, or ground-based cameras to assess crop health. They can detect early signs of disease, nutrient deficiencies, or pest infestations, often before they are visible to the unassisted eye. This allows for timely interventions, reducing crop loss and optimizing resource allocation.

• What Are Vision Language Models (VLMs)? - IBM

  What Are Vision Language Models (VLMs)? | IBM =============== My IBM Log in Subscribe What are vision language models (VLMs)? ======================================= 25 February 2025 #### Share Link copied ? --------------------------------------- Vision language models (VLMs) areartificial intelligence (AI) models that blendcomputer vision andnatural language processing (NLP) capabilities. [...] VLMs learn to map the relationships between text data and visual data such as images or videos, allowing these models to generate text from visual inputs or understand natural language prompts in the context of visual information. VLMs, also referred to as visual language models, combinelarge language models (LLMs) with vision models or visualmachine learning(ML) algorithms. [...] This can be valuable forpredictive maintenance, for example, helping analyze images or videos of factory floors to detect potential equipment defects in real time. ### Object detection Vision language models can recognize and classify objects within an image and provide contextual descriptions such as an object’s position relative to other visual elements.

• Guide to Vision-Language Models (VLMs) - Encord

  A vision-language model is a fusion of vision and natural language models. It ingests images and their respective textual descriptions as inputs and learns to associate the knowledge from the two modalities. The vision part of the model captures spatial features from the images, while the language model encodes information from the text. [...] One exciting application of multimodal AI is Vision-Language Models (VLMs). These models can process and understand the modalities of language (text) and vision (image) simultaneously to perform advanced vision-language tasks, such as Visual Question Answering (VQA), image captioning, and Text-to-Image search. In this article, you will learn about: Let’s start by understanding what vision-language models are. ## What Are Vision Language Models? [...] Please enable JavaScript to view this site. Contents ## What Are Vision Language Models? ## Vision Language Models: Architectures and Popular Models ## Evaluating Vision Language Models ## Datasets for Vision Language Models ## Limitations of Vision Language Models ## Applications of Vision Language Models ## Future Research ## Vision-Language Models: Key Takeaways ###### Encord Blog # Guide to Vision-Language Models (VLMs) blog image blog image encord logo Better Data, Better AI

• Explore Top Computer Vision Models - Roboflow

  Resources Image 19 Customer Stories Image 20 Weekly Product Webinar Image 21 User Forum Image 22 Inference Templates Image 23 Documentation Image 24 Model Playground Image 25 Changelog Image 26 Convert Annotation Formats PricingDocsBlog Sign InBook a demoGet Started Sign inBook a demoGet Started Computer Vision Models ====================== Explore state-of-the-art computer vision model architectures, immediately usable for training with your custom dataset. [...] Apache 2.0 License license • Released Oct 24, 2023 View Model Details Deploy with free GPU false Visual Question Answering Image Tagging Image Captioning LLMS with Vision Capabilities Multimodal Vision Foundation Vision Image 222 Image 223: OpenAI GPT-4 with Vision ----------------- GPT-4 with Vision is a multimodal language model developed by OpenAI. Object Detection Deploy with Roboflow • Image 224: GitHub Repo • stars • license • Released View Model Details [...] Documentation User Forum Changelog What is computer vision? Weekly Product Webinar Convert Annotation Formats Computer Vision Models Model Playground Industries Customer Stories Aerospace & Defense Automative Consumer Goods Energy & Utilities Healthcare & Medicine Industrial Manufacturing Logistics Manufacturing Media & Entertainment Retail & Service Transportation Warehousing Models

• Top 30+ Computer Vision Models For 2025 - Analytics Vidhya

  While traditional CNNs laid the foundation, the field has since embraced new architectures such as vision transformers (ViT, DeiT, Swin Transformer) and multimodal models like CLIP, which have further expanded the capabilities of computer vision systems. These models are increasingly used in applications that require cross-modal understanding by combining visual and textual data. They drive innovative solutions in image captioning, visual question answering, and beyond. [...] In the early days, computer vision was primarily about recognizing handwritten digits on the MNIST dataset. These models were simple yet revolutionary, as they demonstrated that machines could learn useful representations from raw pixel data. One of the first breakthroughs was LeNet (1998), designed by Yann LeCun. [...] Dual-Encoder Design: CLIP employs two separate encoders—one for images (typically a vision transformer or CNN) and one for text (a transformer). Contrastive Learning: The model is trained to maximize the similarity between matching image–text pairs while minimizing the similarity for mismatched pairs, effectively aligning both modalities in a shared latent space.