Microsoft MORA: Multi-Agent Video Generation Framework

With data becoming a pillar stone of a company’s growth strategy, the market for visualization tools is growing rapidly, with a projected compound annual growth rate (CAGR) of 10.07% between 2023 and 2028. The primary driver of these trends is the need for data-driven decision-making, which involves understanding complex data patterns and extracting actionable insights to improve operational efficiency.  PowerBI and Tableau are traditional tools with interactive workspaces for creating intuitive dashboards and exploring large datasets. However, other platforms are emerging to address the ever-changing nature of the modern data ecosystem. In this article, we will discuss the visualizations offered by Databricks - a modern enterprise-scale platform for building data, analytics, and artificial intelligence (AI) solutions. Databricks Databricks is an end-to-end data management and model development solution built on Apache Spark. It lets you create and deploy the latest generative AI (Gen AI) and large language models (LLMs). The platform uses a proprietary Mosaic AI framework to streamline the model development process. It provides tools to fine-tune LLMs seamlessly through enterprise data and offers a unified service for experimentation through foundation models. In addition, it features Databricks SQL, a state-of-the-art lakehouse for cost-effective data storage and retrieval. It lets you centrally store all your data assets in an open format, Delta Lake, for effective governance and discoverability. Further, Databricks SQL has built-in support for data visualization, which lets you extract insights from datasets directly from query results in the SQL editor. Users also benefit from the visualization tools featured in Databricks Notebooks, which help you build interactive charts by using the Plotly library in Python. Through these visualizations, Databricks offers robust data analysis for monitoring data assets critical to your AI models. So, let’s discuss in more detail the types of chart visualizations, graphs, diagrams, and maps available on Databricks to help you choose the most suitable visualization type for your use case. Effective visualization can help with effortless data curation. Learn more about how you can use data curation for computer vision Visualizations in Databricks As mentioned earlier, Databricks provides visualizations through Databricks SQL and Databricks Notebooks. The platform lets you run multiple SQL queries to perform relevant aggregations and apply filters to visualize datasets according to your needs. Databricks also allows you to configure settings related to the X and Y axes, legends, missing values, colors, and labels. Users can also download visualizations in PNG format for documentation purposes. The following sections provide an overview of the various visualization types available in these two frameworks, helping you select the most suitable option for your project. Bar Chart Bar charts are helpful when you want to compare the frequency of occurrence of different categories in your dataset. For instance, you can draw a bar chart to compare the frequency of various age groups, genders, ethnicities, etc. Additionally, bar charts can be used to view the sum of the prices of all orders placed in a particular month and group them by priority. Bar chart The result will show the months on the X-axis and the sum of all the orders categorized by priority on the Y-axis. Line Line charts connect different data points through straight lines. They are helpful when users want to analyze trends over some time. The charts usually show time on the X-axis and some metrics whose trajectory you want to explore on the Y-axis. Line chart For instance, you can view changes in the average price of orders over the years grouped by priority. The trends can help you predict the most likely future values, which can help you with financial projections and budget planning. Pie Chart Pie charts display the proportion of different categories in a dataset. They divide a circle into multiple segments, each showing the proportion of a particular category, with the segment size proportional to the category’s percentage of the total. Pie chart For instance, you can visualize the proportion of orders for each priority. The visualization is helpful when you want a quick overview of data distribution across different segments. It can help you analyze demographic patterns, market share of other products, budget allocation, etc. Scatter Plot A scatter plot displays each data point as a dot representing a relationship between two variables. Users can also control the color of each dot to reflect the relationship across different groups. Scatter Plot For instance, you can plot the relationship between quantity and price for different color-coded item categories. The visualization helps in understanding the correlation between two variables. However, users must interpret the relationship cautiously, as correlation does not always imply causation. Deeper statistical analysis is necessary to uncover causal factors. Area Charts Area charts combine line and bar charts by displaying lines and filling the area underneath with colors representing particular categories. They show how the contribution of a specific category changes relative to others over time. Area Charts For instance, you can visualize which type of order priority contributed the most to revenue by plotting the total price of different order priorities across time. The visualization helps you analyze the composition of a specific metric and how that composition varies over time. It is particularly beneficial in analyzing sales growth patterns for different products, as you can see which product contributed the most to growth across time. Box Chart Box charts concisely represent data distributions of numerical values for different categories. They show the distribution’s median, skewness, interquartile, and value ranges. Box Chart For instance, the box can display the median price value through a line inside the box and the interquartile range through the top and bottom box enclosures. The extended lines represent minimum and maximum price values to compute the price range. The chart helps determine the differences in distribution across multiple categories and lets you detect outliers. You can also see the variability in values across different categories and examine which category was the most stable. Bubble Chart Bubble charts enhance scatter plots by allowing you to visualize the relationship of three variables in a two-dimensional grid. The bubble position represents how the variable on the X-axis relates to the variable on the Y-axis. The bubble size represents the magnitude of a third variable, showing how it changes as the values of the first two variables change. Bubble chart The visualization is helpful for multi-dimensional datasets and provides greater insight when analyzing demographic data. However, like scatter plots, users must not mistake correlation for causation. Combo Chart Combo charts combine line and bar charts to represent key trends in continuous and categorical variables. The categorical variable is on the X-axis, while the continuous variable is on the Y-axis. Combo Chart For instance, you can analyze how the average price varies with the average quantity according to shipping date. The visualization helps summarize complex information involving relationships between three variables on a two-dimensional graph. However, unambiguous interpretation requires careful configuration of labels, colors, and legends. Heatmap Chart Heatmap charts represent data in a matrix format, with each cell having a different color according to the numerical value of a specific variable. The colors change according to the value intensity, with lower values typically having darker and higher values having lighter colors. Heatmap chart For instance, you can visualize how the average price varies according to order priority and order status. Heatmaps are particularly useful in analyzing correlation intensity between two variables. They also help detect outliers by representing unusual values through separate colors. However, interpreting the chart requires proper scaling to ensure colors do not misrepresent intensities. Histogram Histograms display the frequency of particular value ranges to show data distribution patterns. The X-axis contains the value ranges organized as bins, and the Y-axis shows the frequency of each bin. Histogram For instance, you can visualize the frequency of different price ranges to understand price distribution for your orders. The visualization lets you analyze data spread and skewness. It is beneficial in deeper statistical analysis, where you want to derive probabilities and build predictive models. Pivot Tables Pivot tables can help you manipulate tabular displays through drag-and-drop options by changing aggregation records. The option is an alternative to SQL filters for viewing aggregate values according to different conditions. Pivot Tables For instance, you can group total orders by shipping mode and order category. The visualization helps prepare ad-hoc reports and provides important summary information for decision-making. Interactive pivot tables also let users try different arrangements to reveal new insights. Choropleth Map Visualization Choropleth map visualization represents color-coded aggregations categorized according to different geographic locations. Regions with higher value intensities have darker colors, while those with lower intensities have lighter shades. Choropleth map visualization For instance, you can visualize the total revenue coming from different countries. This visualization helps determine global presence and highlight disparities across borders. The insights will allow you to develop marketing strategies tailored to regional tastes and behavior. Funnel Visualization Funnel visualization depicts data aggregations categorized according to specific steps in a pipeline. It represents each step from top to bottom with a bar and the associated value as a label overlay on each bar. It also displays cumulative percentage values showing the proportion of the aggregated value resulting from each stage. Funnel Visualization For instance, you can determine the incoming revenue streams at each stage of the ordering process. This visualization is particularly helpful in analyzing marketing pipelines for e-commerce sites. The tool shows the proportion of customers who view a product ad, click on it, add it to the cart, and proceed to check out. Cohort Analysis Cohort analysis offers an intuitive visualization to track the trajectory of a particular metric across different categories or cohorts. Cohort Analysis For instance, you can analyze the number of active users on an app that signed up in different months of the year. The rows will depict the months, and the columns will represent the proportion of active users in a particular cohort as they move along each month. The visualization helps in retention analysis as you can determine the proportion of retained customers across the user lifecycle. Counter Display Databricks allows you to configure a counter display that explicitly shows how the current value of a particular metric compares with the metric’s target value. Counter display For instance, you can check how the average total revenue compares against the target value. In Databricks, the first row represents the current value, and the second is the target. The visualization helps give a quick snapshot of trending performance and allows you to quantify goals for better strategizing. Sankey Diagrams Sankey diagrams show how data flows between different entities or categories. It represents flows through connected links representing the direction, with entities displayed as nodes on either side of a two-dimensional grid. The width of the connected links represents the magnitude of a particular value flowing from one entity to the other. Sankey Diagram For instance, you can analyze traffic flows from one location to the other. Sankey diagrams can help data engineering teams analyze data flows from different platforms or servers. The analysis can help identify bottlenecks, redundancies, and resource constraints for optimization planning. Sunburst Sequence The sunburst sequence visualizes hierarchical data through concentric circles. Each circle represents a level in the hierarchy and has multiple segments. Each segment represents the proportion of data in the hierarchy. Furthermore, it color codes segments to distinguish between categories within a particular hierarchy. Sunburst Sequence For instance, you can visualize the population of different world regions through a sunburst sequence. The innermost circle represents a continent, the middle one shows a particular region, and the outermost circle displays the country within that region. The visualization helps data science teams analyze relationships between nested data structures. The information will allow you to define clear data labels needed for model training. Table A table represents data in a structured format with rows and columns. Databricks offers additional functionality to hide, reformat, and reorder data. Tables help summarize information in structured datasets. You can use them for further analysis through SQL queries. Word Cloud Word cloud visualizations display words in different sizes according to their frequency in textual data. For instance, you can analyze customer comments or feedback and determine overall sentiment based on the highest-occurring words. Word Cloud While word clouds help identify key themes in unstructured textual datasets, they can suffer from oversimplification. Users must use word clouds only as a quick overview and augment textual analysis with advanced natural language processing techniques. Visualization is critical to efficient data management. Find out the top tools for data management for computer vision Visualizations in Databricks: Key Takeaways With an ever-increasing data volume and variety, visualization is becoming critical for quickly communicating data-based insights in a simplified manner. Databricks is a powerful tool with robust visualization types for analyzing complex datasets. Below are a few key points to remember regarding visualization in Databricks. Databricks SQL and Databricks Notebooks: Databricks offers advanced visualizations through Databricks SQL and Databricks Notebooks as a built-in functionality. Visualization configurations: Users can configure multiple visualization settings to produce charts, graphs, maps, and diagrams per their requirements. Visualization types: Databricks offers multiple visualizations, including bar charts, line graphs, pie charts, scatter plots, area graphs, box plots, bubble charts, combo charts, heatmaps, histograms, pivot tables, choropleth maps, funnels, cohort tables, counter display, Sankey diagrams, sunburst sequences, tables, and word clouds.

Panoptic Segmentation Updates in Encord Over the past 6 months, we have updated and built new features within Encord with a strong focus on improving your panoptic segmentation workflows across data, labeling, and model evaluation. Here are some updates we’ll cover in this article: Bitmask lock. SAM + Bitmask lock + Brush for AI-assisted precision labeling. Fast and performant rendering of fully bitmask-segmented images and videos. Panoptic Quality model evaluation metrics. Bitmask Lock within Encord Annotate to Manage Segmentation Overlap Our Bitmask Lock feature introduces a way to prevent segmentation and masks from overlapping, providing pixel-perfect accuracy for your object segmentation tasks. By simply toggling the “Bitmask cannot be drawn over” button, you can prevent any part of a bitmask label from being included in another label. This feature is crucial for applications requiring precise object boundaries and pixel-perfect annotations, eliminating the risk of overlapping segmentations. Let’s see how to do this within Encord Annotate: Step 1: Create your first Bitmask Initiating your labeling process with the Bitmask is essential for creating precise object boundaries. If you are new to the Bitmask option, check out our quickstart video walkthrough on creating your first Bitmask using brush tools for labeling. Step 2: Set Bitmask Overlapping Behavior  Managing how bitmasks overlap is vital for ensuring accurate segmentation, especially when dealing with multiple objects that are close to each other or overlapping. After creating your first bitmask, adjust the overlapping behavior settings to dictate how subsequent bitmasks interact with existing ones. This feature is crucial for delineating separate objects without merging their labels—perfect for panoptic segmentation. This prevents any part of this bitmask label from being included in another label. This is invaluable for creating high-quality datasets for training panoptic segmentation models. Step 3: Lock Bitmasks When Labeling Multiple Instances Different images require different approaches. Beyond HSV, you can use intensity values for grayscale images (like DICOM) or RGB for color-specific labeling. This flexibility allows for tailored labeling strategies that match the unique attributes of your dataset. Experiment with the different settings (HSV, intensity, and RGB) to select the best approach for your specific labeling task. Adjust the criteria to capture the elements you need precisely. Step 4: Using the Eraser Tool Even with careful labeling, adjustments may be necessary. The eraser tool can remove unwanted parts of a bitmask label before finalizing it, providing an extra layer of precision. If you've applied a label inaccurately, use the eraser tool to correct any errors by removing unwanted areas of the bitmask. See our documentation to learn more. Bitmask-Segmented Images and Videos Got a Serious Performance Lift (At Least 5x) Encord's commitment to enhancing user experience and efficiency is evident in the significant performance improvements made to the Bitmask-segmented annotation within the Label Editor. Our Engineering team has achieved a performance lift of at least 5x by directly addressing user feedback and pinpointing critical bottlenecks. This improves how fast the editor loads for your panoptic segmentation labeling instances.  Here's a closer look at the differences between the "before" and "after" scenarios, highlighting the advancements: Before the Performance Improvements: Performance Lag on Zoom: Users experienced small delays when attempting to zoom in on images, with many instances (over 100) that impacted the precision and speed of their labeling process. Slow Response to Commands: Basic functionalities like deselecting tools or simply navigating through the label editor were met with sluggish responses. Operational Delays: Every action, from image loading to applying labels, was hindered by "a few milliseconds" of delay, which accumulated significant time overheads across projects. After the Performance Enhancements: Quicker Image Load Time: The initial step of image loading has seen a noticeable speed increase! This sets a good pace for the entire labeling task. Responsiveness: The entire label editor interface, from navigating between tasks to adjusting image views, is now remarkably more responsive. This change eradicates previous lag-related frustrations and allows for a smoother user experience. Improved Zoom Functionality: Zooming in and out has become significantly more fluid and precise. This improvement is precious for detailed labeling work, where accuracy is paramount. The positive changes directly result from the Engineering team's responsiveness to user feedback. Our users have renewed confidence in handling future projects with the Label Editor. We are dedicated to improving Encord based on actual user experiences. Use Segment Anything Model (SAM) and Bitmask Lock for High Annotation Precision Starting your annotation process can be time-consuming, especially for complex images. Our Segment Anything Model (SAM) integration offers a one-click solution to create initial annotations. SAM identifies and segments objects in your image, significantly speeding up the annotation process while ensuring high accuracy. Step 1: Select the SAM tool from the toolbar with the Bitmask Lock enabled.  Step 2: Click on the object you wish to segment in your image. SAM will automatically generate a precise bitmask for the object. Step 3: Use the bitmask brush to refine the edges for pixel-perfect segmentation if needed. See how to use the Segment Anything Model (SAM) within Encord in our documentation.   Validate Segmentation with Panoptic Quality Metrics You can easily evaluate your segmentation model’s panoptic mask quality with new metrics:  mSQ (mean Segmentation Quality) mRQ (mean Recognition Quality) mPQ (mean Panoptic Quality) The platform will calculate mSQ, mRQ, and mPQ for your predictions, labels, and dataset to clearly understand the segmentation performance and areas for improvement. Navigate to Active → Under the Model Evaluation tab, choose the panoptic model you want to evaluate. Under Display, toggle the Panoptic Quality Metrics (still in beta) option to see the model's mSQ, mRQ, and mPQ scores. Fast Rendering of Fully Bitmask-Segmented Images within Encord Active The performance improvement within the Label Editor also translates to how you view and load panoptic segmentation within Active.  Try it yourself: Key Takeaways: Panoptic Segmentation Updates in Encord Here’s a recap of the key features and improvements within Encord that can improve your Panoptic Segmentation workflows across data and models: Bitmask Lock: This feature prevents overlaps in segmentation. it guarantees the integrity of each label, enhancing the quality of the training data and, consequently, the accuracy of machine learning models. This feature is crucial for projects requiring meticulous detail and precision. SAM + Bitmask Lock + Brush: The Lock feature allows you to apply Bitmasks to various objects within an image, which reduces manual effort and significantly speeds up your annotation process. The integration of SAM within Encord's platform, using Lock to manage Bitmask overlaps, and the generic brush tool empower you to achieve precise, pixel-perfect labels with minimal effort. Fast and Performant Rendering of Fully Bitmask-segmented Images and Videos: We have made at least 5x improvements to how Encord quickly renders fully Bitmask-segmented images and videos across Annotate Label Editor and Active. Panoptic Quality Model Evaluation Metrics: The Panoptic Quality Metrics—comprising mean Segmentation Quality (mSQ), mean Recognition Quality (mRQ), and mean Panoptic Quality (mPQ)—provide a comprehensive framework for evaluating the effectiveness of segmentation models.

Qwen-VL is a series of open-source large vision-language models (LVLMs), offering a potent combination of advanced capabilities and accessibility. As an open-source project, Qwen-VL not only democratizes access to cutting-edge AI technology but also positions itself as a formidable competitor to established models from tech giants like OpenAI’s GPT-4V and Google’s Gemini. In the competitive landscape of LVLMs, Qwen-VL has quickly risen to the forefront, securing its place as a leader on the OpenVLM leaderboard. This leaderboard, which encompasses 38 different VLMs including GPT-4V, Gemini, QwenVLPlus, LLaVA, and others, serves as a comprehensive benchmark for evaluating model performance across 13 distinct multimodal tasks. OpenVLM Leaderboard Qwen-VL's performance across these benchmarks underscores its versatility and robustness in handling various vision-language tasks with unparalleled accuracy and efficiency. By leading the charge on the OpenVLM leaderboard, Qwen-VL sets a new standard for excellence in the field, pushing the boundaries of what is possible with LVLMs and paving the way for future advancements in multimodal AI research. Introduction to Large-scale Vision Language Models (LVLMs) Large Language Models (LLMs) have attracted attention in recent years for their remarkable text generation and comprehension capabilities in the field of generative AI. However, their limitation to processing text alone has constrained their utility in various applications. In response to this limitation, a new class of models known as Large Vision Language Models (LVLMs) has come up, aiming to integrate visual data with textual information to address vision-centric tasks. LVLMs improve conventional LLMs by integrating vision language learning, thus extending their applicability to include image datasets. However, despite their promising potential, open-source LVLM implementations encounter hurdles such as inadequate training and optimization when compared to proprietary models. Also, understanding visual content still remains a significant challenge for existing LVLM frameworks. Overview of Qwen-VL The Qwen-VL series represents a significant advancement in Large Vision Language Models (LVLMs), designed to overcome the limitations of existing models and equip LLMs with visual processing capabilities. Built upon the Alibaba Cloud’s 7 billion parameter model, Qwen-7B language model, the Qwen-VL series introduces a visual receptor architecture comprising a language-aligned visual encoder and a position-aware adapter. This architecture enables Qwen-VL models to effectively process visual inputs, generate responses based on prompts, and perform various vision-language tasks such as image recognition, image captioning, visual question answering, and visual grounding. Qwen-VL models demonstrate leading performance on vision-centric benchmarks and support multiple languages, including English and Chinese. For more information on VLMs, read the blog Guide to Vision-Language Models (VLMs)   Key Features of Qwen-VL Qwen-VL models demonstrate good accuracy on a wide range of vision-centric understanding benchmarks, surpassing other SOTA models of similar scales. They excel not only in conventional benchmarks such as captioning and question-answering but also in recently introduced dialogue benchmarks. Here are the key features of Qwen-VL: Multi-lingual Support: Similar to Qwen-LM, Qwen-VLs are trained on multilingual image-text data, with a substantial corpus in English and Chinese. This enables Qwen-VLs to naturally support English, Chinese, and other multilingual instructions. Multi-image Capability: During training, Qwen-VLs can handle arbitrary interleaved image-text data as inputs, allowing them to compare, understand, and analyze context when multiple images are provided. Fine-grained Visual Understanding: Qwen-VLs exhibit highly competitive fine-grained visual understanding abilities, thanks to their higher-resolution input size and fine-grained corpus used during training. Compared to existing vision-language generalists, Qwen-VLs demonstrate superior performance in tasks such as grounding, text-reading, text-oriented question answering, and fine-grained dialogue comprehension. Vision-centric Understanding: This allows the model to comprehensively interpret and process visual information. With advanced architecture integrating a language-aligned visual encoder and position-aware adapter, Qwen-VL excels in tasks like image captioning, question answering, and visual grounding. Its fine-grained analysis ensures precise interpretation of visual content, making Qwen-VL highly effective in vision-language tasks and real-world applications. Design Structure of Qwen-VL Beginning with the foundation of Qwen-LM, the model is enhanced with visual capacity through several key components: Visual Receptor: Qwen-VL incorporates a carefully designed visual receptor, which includes a visual encoder and adapter. This component is responsible for processing image inputs and extracting fixed-length sequences of image features.  Input-Output Interface: The model's input-output interface is optimized to differentiate between image and text feature inputs. Special tokens are utilized to delineate image feature input, ensuring seamless integration of both modalities. 3-stage Training Pipeline: Qwen-VL employs a sophisticated 3-stage training pipeline to optimize model performance. This pipeline encompasses comprehensive training stages aimed at fine-tuning the model's parameters and enhancing its ability to comprehend and generate responses for both text and image inputs. Multilingual Multimodal Cleaned Corpus: Qwen-VL is trained on a diverse multilingual multimodal corpus, which includes cleaned data encompassing both textual and visual information. This corpus facilitates the model's ability to understand and generate responses in multiple languages while effectively processing various types of visual content. Model Architecture of Qwen-VL The architecture of Qwen-VL comprises three key components, each contributing to the model's robustness in processing both text and visual inputs.  Large Language Model Qwen-VL leverages a large language model as its foundational component. This machine learning model is initialized with pre-trained weights obtained from Qwen-7B, ensuring a strong linguistic foundation for the model's language processing capabilities. Visual Encoder Qwen-VL employs the Vision Transformer (ViT) architecture, utilizing pre-trained weights from Openclip's ViT-bigG. During both training and inference, input images are resized to a specific resolution. The visual encoder processes these images by dividing them into patches with a stride of 14, thereby generating a set of image features that encapsulate visual information. Position-aware Vision-Language Adapter To address efficiency concerns arising from long sequences of image features, Qwen-VL introduces a vision-language adapter. This adapter is designed to compress the image features, enhancing computational efficiency. It consists of a single-layer cross-attention module initialized randomly. This module utilizes a group of trainable embeddings as query vectors and the image features from the visual encoder as keys for cross-attention operations. By employing this mechanism, the visual feature sequence is compressed to a fixed length of 256. To preserve positional information crucial for fine-grained image comprehension, 2D absolute positional encodings are incorporated into the query-key pairs of the cross-attention mechanism. This ensures that positional details are retained during the compression process. The compressed image feature sequence of length 256 is then fed into the large language model, enabling Qwen-VL to effectively process both textual and visual inputs and perform a wide range of vision-language tasks with high accuracy and efficiency. Training Pipeline of Qwen-VL series For more information, read the official paper released on Arxiv: Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.   Performance of Qwen-VL against State-of-The-Art LVLMs The performance of Qwen-VL models, particularly Qwen-VL-Max, surpasses SOTA models such as Gemini Ultra and GPT-4V in various text-image multimodal tasks. Compared to the open-source version of Qwen-VL, these models achieve comparable results to Gemini Ultra and GPT-4V, while significantly outperforming previous best results from open-source models. Performance of Qwen-VL-Plus and Qwen-VL-Max against other LVLM In particular, Qwen-VL-Max demonstrates superior performance over GPT-4V from OpenAI and Gemini from Google in tasks related to Chinese question answering and Chinese text comprehension. This achievement highlights the advanced capabilities of Qwen-VL-Max and its potential to establish new benchmarks in multimodal AI research and application. It should also be noted that most SOTA models are not trained on chinese language. Capabilities of Qwen-VL Qwen-VL exhibits a diverse range of capabilities that enable it to effectively comprehend and interact with visual and textual information, as well as reason and learn from its environment. These capabilities include: Basic Recognition Capabilities Qwen-VL demonstrates strong basic recognition capabilities, accurately identifying and describing various elements within images, including common objects, celebrities, landmarks, and intricate details. Recognition capabilities of Qwen-VL Visual Agent Capability As a visual agent, Qwen-VL is capable of providing detailed background information, answering questions, and analyzing complex visual content. It can also compose poetry in multiple languages inspired by visual stimuli and analyze everyday screenshots. Visual Agent Capabilities of Qwen-VL Visual Reasoning Capability Qwen-VL possesses advanced visual reasoning capabilities, extending beyond content description to comprehend and interpret intricate representations such as flowcharts, diagrams, and other symbolic systems. It excels in problem-solving and reasoning tasks, including mathematical problem-solving and profound interpretations of charts and graphs. Qwen-VL has advanced visual reasoning capabilities Text Information Recognition and Processing Qwen-VL exhibits enhanced text information recognition and processing abilities, efficiently extracting information from tables and documents, reformatting it to meet customized output requirements, and effectively identifying and converting dense text. It also supports images with extreme aspect ratios, ensuring flexibility in processing diverse visual content. Advanced text information recognition and processing abilities of Qwen-VL Few-shot Learning on Vision-Language Tasks Qwen-VL demonstrates satisfactory in-context learning (few-shot learning) ability, achieving superior performance on vision-language tasks such as question answering and image captioning compared to models with similar numbers of parameters. Its performance rivals even larger models, showcasing its adaptability and efficiency in learning from limited data. For more information on few-shot learning, read the blog Few Shot Learning in Computer Vision: Approaches & Uses   Qwen-VL Availability Qwen-VL, including Qwen-VL-Plus and Qwen-VL-Max, is now readily accessible through various platforms, offering researchers and developers convenient access to its powerful capabilities: HuggingFace: Users can access Qwen-VL-Plus and Qwen-VL-Max through the Huggingface Spaces and Qwen website, enabling seamless integration into their projects and workflows. Dashscope APIs: The APIs of Qwen-VL-Plus and Qwen-VL-Max are available through the Dashscope platform, providing developers with the flexibility to leverage its capabilities for their AI applications. Detailed documentation and quick-start guides are available on the Dashscope platform for easy integration. QianWen Web Portal: By logging into the Tongyi QianWen web portal and switching to "Image Understanding" mode, users can harness the latest Qwen-VL-Max capabilities for image understanding tasks. This mode offers additional functionalities tailored specifically for image processing and understanding. ModelScope: The Qwen-VL-Chat demo is available on modelscope. GitHub Repository: The code and model weights of both Qwen-VL and Qwen-VL-Chat are openly available to download on GitHub, allowing researchers and developers to explore, modify, and utilize them freely. The commercial use of these resources is permitted, enabling their integration into commercial projects and applications. Qwen-VL-Chat Qwen-VL-Chat, as a generalist multimodal LLM-based AI assistant, supports complex interactions, including multiple image inputs, multi-round question answering, and creative capabilities. Unlike traditional vision-language chatbots, Qwen-VL-Chat's alignment techniques enable it to comprehend and respond to complex visual and textual inputs with superior accuracy and flexibility. Here's how Qwen-VL-Chat stands out in real-world dialog benchmarks and compares with existing models: Qwen-VL-Chat Vs. Vision-Language Chat Performance of Qwen-VL against other generalist models across various tasks Qwen-VL-Chat's advanced capabilities are evaluated using the TouchStone benchmark, which assesses overall text-image dialogue capability and alignment with humans. Unlike conventional models like chatGPT or Bard, Qwen-VL-Chat excels in handling direct image input, thanks to fine-grained image annotations provided by human labeling. With a comprehensive coverage of 300+ images, 800+ questions, and 27 categories, including attribute-based Q&A, celebrity recognition, writing poetry, summarizing multiple images, product comparison, and math problem solving, Qwen-VL-Chat achieves superior performance in understanding and responding to complex visual and textual inputs. You can find the official tutorial to implement Qwen-VL-Chat on your own on Github.   Real-world Dialog Benchmark Qwen-VL-Chat's outstanding results in other multimodal benchmarks, such the MME Benchmark and Seed-Bench, demonstrate that its performance evaluation extends beyond the TouchStone benchmark. In both the perceptual and cognition tracks, Qwen-VL-Chat obtains state-of-the-art scores in the MME Benchmark, an extensive evaluation of multimodal large language models. The Qwen series, which includes Qwen-VL-Chat, achieves state-of-the-art performance in Seed-Bench, a benchmark consisting of 19K multiple-choice questions with precise human annotations. Qwen-VL: What’s Next? The release of the Qwen-VL series represents a significant stride forward in large-scale multilingual vision-language models, with the goal of advancing multimodal research.  Qwen-VL has demonstrated its superiority over comparable artificial intelligence models across various benchmarks, facilitating multilingual complex conversations, multi-image interleaved conversations, grounding in Chinese, and fine-grained recognition. Looking ahead, the focus is on further enhancing Qwen-VL's capabilities in several key dimensions: Multi-modal Generation The team plans to integrate Qwen-VL with more modalities, including speech and video. By expanding its scope to encompass these modalities, Qwen-VL will enhance its ability to understand and generate content across a wider range of inputs. Multi-Modal Generation This generative AI model will be further developed to excel in multi-modal generation, particularly in generating high-fidelity images and fluent speech. By enhancing its ability to generate content across multiple modalities with high fidelity and fluency, Qwen-VL will advance the state-of-the-art in multimodal AI systems. Augmentation of Model Size and Training Data Efforts are underway to scale up the model size, training data, and resolution of Qwen-VL. This enhancement aims to enable Qwen-VL to handle more complex and intricate relationships within multimodal data, leading to more nuanced and comprehensive understanding and generation of content.