#Computer Vision
Articles about Computer Vision — exploring patterns, best practices, and real-world implementations in production systems.
15 posts tagged with computer vision. ← All posts
A computer-use agent can perceive the screen and decide "click Submit," but it still has to answer a deceptively hard question: where exactly is Submit, and how do I act on precisely that spot? This UI grounding problem — connecting a decision to a precise screen location and element — is where many computer-use agents stumble, because a click a few pixels off hits the wrong thing. This post is about how agents locate and target UI elements reliably.
Deciding 'click Submit' is easy; knowing exactly where Submit is and clicking precisely there is the grounding problem — where a click a few pixels off hits the wrong thing. Coordinate vs element-based vs set-of-marks grounding, why precise localization is hard for vision, and why mis-clicks are a top failure mode.
Before a computer-use agent can click anything, it has to understand what's on the screen — and the screen is just an image of pixels designed for human eyes. Turning that image into "there's a submit button here, a text field there, an error message at the top" is the perception problem, and it's where computer-use agents most often succeed or fail. This post is about how agents see a GUI, the two main approaches, and why perception is the foundation everything else rests on.
Before clicking anything, an agent must understand the screen — pixels designed for human eyes. The perception problem, the two approaches (vision-based multimodal interpretation vs structured accessibility/DOM data), why hybrid wins, and why perception is the foundation most failures trace back to.
Building a computer-vision system that works in a demo is one thing; building one that works reliably in the real world — at the right speed, cost, and robustness — is another. This closing post assembles the series into practical judgment: how to approach a vision problem, the deployment realities that trip teams up, and where the field is heading. It turns pixels-to-understanding from a concept into something you can actually ship.
Building a vision demo is one thing; shipping a reliable system at the right speed, cost, and robustness is another. A workflow for approaching a vision problem, the deployment realities (efficiency for real-time/edge, robustness to distribution shift, edge cases, monitoring), and where the field is heading — multimodal and foundation models.
Almost nobody trains a computer-vision model from scratch — and if you're trying to, you're probably doing it wrong. The real practice of vision is transfer learning: take a model pretrained on millions of images, adapt it to your task with a fraction of the data. This, plus smart data strategy, is how vision gets built in the real world, and it's the single most practical thing to understand about applying computer vision. This post is about how it's actually done.
Almost nobody trains vision models from scratch — the real practice is transfer learning: adapt a pretrained model with a fraction of the data. Feature extraction vs fine-tuning (chosen by data size), and the data realities (labeling cost, representativeness, augmentation) that actually decide a vision project.
For a decade, convolution was synonymous with computer vision. Then the transformer — the architecture that revolutionized language — was applied to images, and the vision transformer showed that attention could match or beat CNNs given enough data. It's a profound shift: the same architecture now underpins both language and vision, enabling the multimodal models that see and read at once. This post explains how transformers handle images and what their rise means.
The vision transformer treats an image as a sequence of patches and runs a standard transformer, giving global context immediately. The deep trade-off (CNN inductive bias vs ViT learning from data — ViTs win at scale) and the big consequence: one architecture now unifies vision and language, enabling multimodal AI.
Classifying a whole image as "cat" is only the beginning of computer vision. Real applications need to know where the cat is, how many objects there are, and sometimes exactly which pixels belong to each one. Object detection and segmentation answer those harder questions, and they power self-driving cars, medical imaging, and photo editing. This post covers the main vision tasks beyond classification and how they build on the CNN foundation.
Real vision needs more than 'what is this?' — the task hierarchy runs classification → detection (bounding boxes) → semantic segmentation (per-pixel) → instance segmentation. How one-stage (YOLO) vs two-stage detectors and encoder-decoders (U-Net) build on the CNN backbone, powering self-driving and medical imaging.
Once convolution proved it could crack vision, the race was on to build better CNNs — and the story of how they improved is a compact lesson in deep learning itself. From AlexNet to VGG to ResNet, each landmark architecture solved a specific problem that was blocking progress, and the solutions (depth, small filters, skip connections) became foundational ideas used far beyond vision. This post traces that evolution and the principles it taught.
From AlexNet (which launched deep learning) to VGG (small filters, deep) to ResNet (skip connections that made very deep networks trainable), each landmark solved a specific blocker. ResNet's residual connection generalized everywhere — including every transformer block — so the arc is a compact lesson in deep learning itself.
The convolutional neural network is the architecture that finally solved computer vision — and it did so with an idea as elegant as it is powerful: slide a small learnable filter across the image, everywhere, looking for patterns. CNNs automated the feature engineering that classical vision did by hand, learning a hierarchy from edges to objects directly from pixels. This post explains how convolution works and why it was exactly the right inductive bias for images.
The CNN solved vision by learning features instead of hand-designing them. Convolution slides a small learnable filter everywhere — local connectivity and parameter sharing give translation invariance for free — and stacking layers builds a hierarchy from edges to objects. The right inductive bias for images.
Before neural networks, computer vision spent decades building an ingenious toolkit: detect edges, find corners, describe local patches, match features across images. These classical methods are no longer state of the art for recognition, but they're far from obsolete — they still power real systems, they illuminate what deep learning automated, and understanding them makes you a better vision engineer. This post covers how vision worked before deep learning, and why it still matters.
Before neural networks, vision hand-engineered features: edges, corners, invariant descriptors (SIFT). These still power matching, panorama stitching, and 3D/SLAM — and reveal exactly what CNNs later automated. Their ceiling on recognition is what the deep-learning breakthrough overcame.
Humans see so effortlessly that it's easy to forget vision is one of the hardest problems in computing. To a computer an image is just a grid of numbers, and turning those numbers into "that's a cat" requires bridging an enormous gap between raw pixels and meaning. This series builds computer vision from first principles — from pixels to classical methods to the deep learning that finally cracked it — and it starts with why the problem is so deceptively hard.
To a computer an image is just a grid of numbers, and turning those into 'that's a cat' means bridging the semantic gap — made brutal by viewpoint, lighting, scale, occlusion, and intra-class variation. Why vision defeats hand-written rules and is fundamentally a learning problem.
Typing a sentence and watching a detailed, coherent, never-before-seen image appear is one of the most striking capabilities in modern AI — and the technique behind it is beautifully counterintuitive. Rather than paint an image stroke by stroke, these models start with pure noise and gradually remove it, step by step, sculpting a picture out of static, guided by your text. Understanding diffusion — and how text steers it — demystifies text-to-image generation and reveals one of the most important generative techniques in AI.
Typing a sentence and watching a detailed, never-before-seen image appear is one of the most striking capabilities in modern AI — and the technique behind it is beautifully counterintuitive. These models start with pure noise and gradually remove it, sculpting a picture out of static, guided by your text. That's diffusion.
The multimodal capability people actually experience — uploading a photo and asking a chatbot what's in it, or having it read a screenshot, explain a diagram, or extract data from a chart — comes from vision-language models: LLMs that can see. The clever part is how it's done. Rather than build a seeing-and-reasoning model from scratch, you take a language model that already reasons brilliantly and give it eyes, by connecting a vision encoder to it. Understanding how that connection works explains the multimodal AI most people use.
The multimodal capability people actually experience — uploading a photo and asking a chatbot what's in it — comes from vision-language models: LLMs that can see. The clever part is how it's done: take a language model that already reasons brilliantly and give it eyes by connecting a vision encoder to it.
The magic trick at the heart of modern multimodal AI is deceptively simple: train an image encoder and a text encoder together so that a picture of a dog and the words "a photo of a dog" land at the same spot in a shared space. Once images and text live in one common representational space, a cascade of capabilities follows — searching images by text, classifying without task-specific training, and grounding language generation in vision. CLIP is the model that made this idea famous, and understanding it is understanding how modalities actually get connected.
The magic trick at the heart of modern multimodal AI is deceptively simple: train an image encoder and a text encoder together so a picture of a dog and the words 'a photo of a dog' land at the same spot in a shared space. Once images and text live in one common space, a cascade of capabilities follows. CLIP is the model that made this famous.
To a computer, an image is just a grid of numbers — millions of pixel values with no inherent meaning. Turning that raw grid into something a model can understand (this is a dog, that's a face, here's text on a sign) is the problem of computer vision, and the way it's solved has changed dramatically. The field moved from hand-crafted feature detectors, to convolutional networks that learn features, to — most recently — the surprising discovery that the transformer architecture behind language models works remarkably well for images too. Understanding how models see is the foundation of the vision side of multimodal AI.
To a computer, an image is just a grid of numbers with no inherent meaning. Turning that raw grid into understanding is computer vision, and the field moved from hand-crafted features, to convolutional networks, to the surprising discovery that the transformer architecture behind language models works remarkably well for images too.
For most of the deep-learning era, an AI model did one thing with one kind of data: this model classifies images, that one translates text, another transcribes speech. Multimodal AI breaks that separation. A single model can now look at an image and describe it, answer questions about a chart, generate a picture from a sentence, or transcribe and reason about audio — because it works across modalities rather than being confined to one. This shift, from single-modality specialists to models that bridge vision, language, audio, and more, is one of the most important developments in modern AI.
For most of deep learning, a model did one thing with one kind of data. Multimodal AI breaks that separation: a single model can look at an image and describe it, generate a picture from a sentence, or transcribe and reason about audio — working across modalities rather than being confined to one. It's one of the most important developments in modern AI.
All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.