AI Is Accelerating At Accelerating Rates
Breaking down the Qwen2.5-VL Technical Report & discussing implications
We have breaking developments from China.
As I called out in the post below, DeepSeek is in danger of being taken out by dark horse Alibaba and their Qwen series of models.
Well this week Qwen dropped a doozy of a model that has taken over the throne in terms of combined visual and language-based benchmarks.
Just like humans need to see with our eyes and reason using language inside our minds… we are starting to optimize models around this architecture.
Very exciting. Of course, they also released a wonderful paper we are going to unpack here.
Alibaba's Qwen Team introduces Qwen2.5-VL, a vision-language model excelling in visual recognition, object localization, document parsing, and video understanding. This model uses dynamic resolution processing and time encoding to handle diverse inputs and leverages window attention for efficiency. Qwen2.5-VL comes in three sizes, with the 72B parameter model rivaling GPT-4o and Claude 3.5 Sonnet, especially in document understanding, while smaller versions offer strong performance in resource-limited settings.
The model architecture features enhancements to the Vision Transformer (ViT) and a large language model, pre-trained on a massive dataset of 4.1 trillion tokens. Post-training involves supervised fine-tuning and direct preference optimization to align the model with human preferences, resulting in state-of-the-art performance across various benchmarks. Qwen2.5-VL demonstrates enhanced reasoning and task execution and sets a new standard for vision-language models.
When I started reading this I had a few questions, like:
“How does Qwen2.5-VL architecture integrate vision and language for enhanced multimodal understanding?”
The Qwen2.5-VL model integrates vision and language through a specific architecture that includes a vision encoder, a vision-language merger, and a large language model.
Let’s look at each piece of the design.
A redesigned Vision Transformer (ViT) architecture serves as the vision encoder. It incorporates 2D-RoPE and window attention to handle native input resolutions efficiently. The input images are resized to multiples of 28 before being processed by the ViT. The ViT splits the images into patches and generates image features. Windowed attention in most layers ensures computational cost scales linearly with the number of patches, while four layers use full self-attention. The vision encoder is trained from scratch through several stages, including CLIP pre-training, vision-language alignment, and end-to-end fine-tuning. Dynamic sampling at native resolutions during training ensures robustness across varying input resolutions
Rather than build a single model, the team built parts of the brain and then fused them together.
The next part of the design is key.
To efficiently manage long sequences of image features, the model employs a simple, effective approach to compress these sequences before they are fed into the LLM — this is the MLP-based Vision-Language Merger.
Spatially adjacent sets of four patch features are grouped, concatenated, and passed through a two-layer multi-layer perceptron (MLP). This projects them into a dimension that aligns with the text embeddings used in the LLM. This method reduces computational costs and provides a flexible way to dynamically compress image feature sequences of varying lengths.
It’s absolutely brilliant to do it this way. Resource efficient. MLPs are decades old tech and this team found a clever way to find the minimum required computational power (at just the right point) in the thinking process to use them to add reasoning power. Truly unreal.
But there are other beautiful parts of this brain we should explore.
The Qwen2.5-VL series uses large language models as its foundational component, initialized with pre-trained weights from the Qwen2.5 LLM. The 1D RoPE (Rotary Position Embedding) is modified to Multimodal Rotary Position Embedding Aligned to Absolute Time to better meet the demands of multimodal understanding.
That’s a lot for non-nerds so let us analogize.
Imagine you're building a robot chef (Qwen2.5-VL). You don't start from scratch, teaching it everything about cooking. Instead, you use a pre-built, very smart "brain" (a large language model) as the robot's foundation. This brain already knows a lot about language and general knowledge, like a very experienced human chef who has read tons of cookbooks (Qwen 2.5 LLM). You give the robot this experienced brain as a starting point. It's like downloading the knowledge from the human chef to the robot.
The original brain (with 1D RoPE) was good at understanding text, like reading recipes in order. Think of 1D RoPE as a way for the robot to keep track of the order of words in a sentence, like steps in a recipe ("first add flour, then add eggs").
But our robot chef needs to understand more than just text. It needs to understand pictures (images of ingredients), maybe even sounds (the sizzle of frying onions). It needs to navigate around the kitchen. This is "multimodal understanding" – seeing, hearing, moving, and reading.
So, we modify the way the robot keeps track of things. We upgrade it to "Multimodal Rotary Position Embedding Aligned to Absolute Time". Imagine the robot is not just reading a line of text, instead, the robot is presented a spread of ingredients with a clock, and each item has a timestamp of when to be used.
The "Absolute Time" part means that the robot now cares exactly when something happens, not just the order. It is no longer relatively positioned, like step 1, step 2. It is now the bread must be added at 1:03, the butter at 1:06, etc.
What The Heck Is Happening
Qwen2.5-VL is a significant upgrade to Alibaba's Qwen series of vision-language models (VLMs). It's designed to be a powerful, versatile AI that can understand and interact with the world using both images/videos and text. Think of it as a highly capable digital assistant that can not only "see" but also deeply comprehend what it's seeing and relate it to language.
The key advancements focus on fine-grained perception. This means the model doesn't just recognize a picture of a cat; it can accurately pinpoint the cat's location within the image (using bounding boxes or points), understand complex documents like invoices or scientific papers (extracting data from tables, charts, and even chemical formulas), and even follow long videos (up to hours!) and identify specific events within seconds.
The paper introduces several technical innovations to achieve this:
Native Dynamic Resolution: The model processes images and videos at their original resolution, without forcing them into a fixed size. This preserves crucial details and allows for better understanding of scale.
Absolute Time Encoding: For videos, the model understands the actual timing of events, not just their order. This is like knowing that something happened at the 3-minute mark, not just after the previous event.
Window Attention: This technique speeds up processing by focusing on relevant parts of an image, similar to how our eyes dart around a scene.
Massive Data Training: The model was trained on a huge dataset (4.1 trillion tokens), including a wide variety of image-text pairings, documents, and videos.
Agent Functionality: The model is prepared to be used in real-world scenarios, for example, allowing a user to perform tasks on their computer, such as clicking certain buttons or filling in forms.
The result is a model that matches or surpasses state-of-the-art models like GPT-4o and Claude 3.5 Sonnet on various benchmarks, particularly excelling in document understanding and fine-grained visual tasks. It's available in different sizes (3B, 7B, and 72B parameters), making it suitable for various applications, from edge devices to high-performance computing. The authors highlight that it maintains strong linguistic abilities, meaning it doesn't sacrifice language understanding for visual capabilities.
Qwen2.5-VL builds upon a foundation of existing VLM research but pushes forward in several crucial ways - ways that help this model work with REAL data we see (variable size, format, resolution, frame rate, etc…).
Many prior VLMs rely on resizing images and videos to a fixed resolution and frame rate. This normalization simplifies processing but sacrifices detail. Qwen2.5-VL's dynamic resolution and FPS handling is a major step forward. It allows the model to inherently understand scale (a small object in a high-resolution image is treated differently than a large object in a low-resolution image) and temporal dynamics (a fast-moving object in a high-FPS video is perceived differently than a slow-moving one). This is closer to how humans perceive the world. Previous methods might use textual timestamps or extra processing steps to try to capture time; Qwen2.5-VL's absolute time encoding integrates this directly into the model's core.
The team worked out a new implementation of attention, too.
Processing high-resolution images and long videos is computationally expensive. Standard "full attention" mechanisms in Transformers (the underlying architecture) have quadratic complexity – meaning the computation grows very rapidly with the input size. Qwen2.5-VL incorporates window attention, which limits the attention mechanism to a local "window" around each part of the input. This drastically reduces computation while still capturing important relationships within the image or video. This is a more efficient way to handle large inputs compared to simply reducing resolution or using smaller models.
While many VLMs excel at general image understanding (e.g., captioning), Qwen2.5-VL specifically targets fine-grained tasks. This includes:
Precise Object Localization: Not just detecting objects, but accurately pinpointing their location with bounding boxes and points, even supporting formats like JSON for integration with other systems.
Document Omni-Parsing: Going beyond simple OCR to understand the structure and meaning of complex documents, including tables, charts, formulas, and handwritten notes. This is a significant improvement over models that treat documents as just a sequence of text and images.
Long-Video Understanding: Handling videos up to hours long and identifying specific events with second-level precision. This requires a deep understanding of temporal relationships and the ability to focus on relevant segments.
Enhanced Agent Functionality: The model is more capable than many previous systems at taking actions within a computer or phone interface, which requires detailed understanding of that interface.
The paper emphasizes the importance of high-quality, diverse training data. They significantly expanded their training corpus and developed a pipeline for filtering and scoring data to ensure relevance and accuracy. This includes specialized data for tasks like document parsing, object grounding, and video understanding. They also use techniques like "rejection sampling" to focus on examples that improve the model's reasoning abilities. This careful data curation is crucial for achieving state-of-the-art performance.
In essence, Qwen2.5-VL is not just a bigger or faster model; it's a model that thinks differently about visual and temporal information, making it more capable of handling complex, real-world tasks.
Imagine you're teaching a student to understand a complex scientific paper.
Old Method (Basic VLM): You give the student a blurry, low-resolution photocopy of the paper and ask them to summarize it. They can probably get the general idea, but they'll miss details in the charts, formulas, and fine print. They might struggle to understand the relationships between different sections.
Qwen2.5-VL Method: You give the student the original, high-resolution paper, a magnifying glass, and a stopwatch. You also teach them how to read charts, understand chemical formulas, and follow the logical flow of the arguments. They can now not only summarize the paper but also answer detailed questions about specific data points, explain the experimental setup, and even identify potential errors in the reasoning. They can also watch a video recording of the experiment and pinpoint exactly when specific events occurred. They can also tell you which buttons to click in a related program, and explain why.
Qwen2.5-VL is like the student with the high-resolution paper, tools, and specialized training. It's designed to understand the details and relationships within complex visual and textual information, not just the overall gist.
Qwen2.5-VL's advancements have significant implications for the future of AI - chiefly the ability to understand fine-grained visual and temporal information opens the door to much more capable AI assistants:
AI Agents that can analyze your medical scans and pinpoint subtle anomalies while perfectly remembering all your medical data and medical history narrative information.
AI Agents that can process your company's invoices and automatically extract key data before sending it to the correct systems for storage and processing.
AI Agents that can watch security camera footage and identify suspicious activity in real-time.
AI Agents that can help you troubleshoot a complex machine by analyzing its diagrams and manuals + see your screen in real-time.
AI Agents that can understand and respond to user interactions on computer and mobile devices, performing tasks on behalf of the user.
Another key factor here: gaining control over time.
The focus on native resolution and absolute time encoding brings AI closer to how humans perceive the world. This could lead to more natural and intuitive interactions with AI systems. For example, you could point to an object in a video and ask the AI, "What happened to this at 2:35?" and get a precise answer.
This isn’t just about AI agents and LLM digital systems… Fine-grained perception is crucial for robots and autonomous vehicles that need to interact with the real world. Qwen2.5-VL's capabilities could enable robots to:
Navigate complex environments with greater precision.
Manipulate objects with greater dexterity.
Understand and respond to human instructions more accurately.
The innovations, especially dynamic sampling, multimodal rotary position encoding, and window attention, may be used to improve and create similar models.
Link to the Qwen research paper: https://arxiv.org/pdf/2502.13923
As you can see, I love ripping apart these papers and figuring out where the alpha comes from and what makes it tick.
Here are two recent write-ups on cutting edge papers in AI:
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