Papers

Imagine you’ve spent months training a sophisticated machine learning model to identify different types of cars in images. It’s brilliant at distinguishing a sedan from an SUV. Now, you’re tasked with a new project: identifying trucks. In a traditional machine learning world, you would have to start all over again—collecting thousands of labeled truck images and training a brand-new model from scratch. It feels wasteful, doesn’t it? All that knowledge your first model learned about edges, wheels, and metallic surfaces seems like it should be useful. ...

In the world of Natural Language Processing (NLP), some research moments feel like earthquakes. They don’t just shift the ground—they reshape the entire landscape. The 2017 paper “Attention Is All You Need” was one such moment. It introduced an architecture that has since become the foundation for nearly every state-of-the-art NLP model, from GPT-3 to BERT. That architecture is the Transformer. Before the Transformer, the go-to models for sequence tasks like machine translation were Recurrent Neural Networks (RNNs), particularly LSTMs and GRUs. These models process text sequentially, word by word, maintaining a hidden state that carries information from the past. While this sequential nature is intuitive, it is also their greatest weakness: it makes them slow to train and notoriously difficult to parallelize. Capturing dependencies between words far apart in a sentence becomes increasingly challenging as sequence length grows. ...

Computer vision has made incredible strides in teaching machines to see. We’ve gone from simply classifying an entire image (“this is a cat”) to detecting individual objects within it (“here is a cat, and here is a dog”). But what if we need more detail? What if, instead of just drawing a box around the cat, we wanted to know the exact pixels that belong to the cat? This is the challenge of instance segmentation—a task that combines two fundamental problems: ...

Imagine a radiologist meticulously scrolling through hundreds of MRI slices, trying to trace the exact boundary of a tumor or an organ. This process, known as segmentation, is fundamental to medical diagnosis, treatment planning, and research. It’s also incredibly time-consuming, tedious, and subject to human error. For years, computer scientists have sought to automate this task, but the complexity of 3D medical data—like MRIs and CT scans—has been a major hurdle. ...

How can we teach a computer to see like a biologist — not just to recognize that an image contains cells, but to outline the precise boundaries of every single one? This task, known as image segmentation, is a cornerstone of biomedical research and diagnostics. It automates the analysis of thousands of microscope images, helps track cancer progression, and maps entire neural circuits. Deep learning models seemed like the perfect tool for this work. Breakthrough architectures such as AlexNet showed that convolutional neural networks (CNNs) could learn powerful visual representations — but they required massive datasets. Training AlexNet involved over a million labeled images. In biomedical imaging, collecting and annotating even a few hundred examples is often expensive and time-consuming. This data scarcity was a serious roadblock. ...

For years, Convolutional Neural Networks (CNNs) have been the undisputed champions of image classification. Give a CNN a picture, and it can tell you with incredible accuracy whether you’re looking at a cat, a dog, or a car. But what if you want to know where the cat is in the picture—not just a bounding box around it, but its exact outline, pixel by pixel? This is the task of semantic segmentation, and it’s a giant leap from classification’s “what” to a much deeper “what and where.” ...

Object detection is one of the foundational tasks in computer vision. It’s the capability that allows computers to not just see an image, but to understand what’s in it—locating and identifying every car, person, bird, and coffee mug in a scene. For years, the R-CNN family of models has been at the forefront of this field. Beginning with R-CNN, then evolving into the much faster Fast R-CNN, these models pushed the boundaries of accuracy. ...

In the world of computer vision, object detection — the task of identifying and localizing objects within an image — is a core challenge for systems that need to interpret visual data. Before 2015, the leading deep learning methods for object detection were accurate but notoriously slow and cumbersome. They involved complex, multi-stage training pipelines that were difficult to optimize and painfully slow to run. This all changed with the introduction of the Fast R-CNN paper by Ross Girshick. ...

In the early 2010s, deep Convolutional Neural Networks (CNNs) like AlexNet sparked a revolution in computer vision, shattering records in image classification. Yet, amidst this breakthrough, a surprisingly rigid constraint held these powerful models back: they demanded that every single input image be exactly the same size—typically something like 224×224 pixels. Think about that for a moment. The real world is filled with images of all shapes and sizes. To make them fit, researchers had to resort to crude methods: either cropping a patch from the image—potentially cutting out the main subject—or warping (stretching or squishing) the image, which distorts its geometry. Both approaches risk throwing away valuable information before the network even sees the image. ...

For years, the field of computer vision was dominated by carefully hand-crafted features. Algorithms like SIFT and HOG were the undisputed champions, forming the backbone of nearly every state-of-the-art object detection system. But by 2012, progress was slowing. Performance on the benchmark PASCAL VOC Challenge had hit a plateau, and it seemed the community was squeezing the last drops of performance from existing methods. A true breakthrough was needed. Meanwhile, in a seemingly separate corner of machine learning, a revolution was brewing. Deep learning—specifically Convolutional Neural Networks (CNNs)—was making waves. The pivotal moment came in 2012 when a CNN called AlexNet demolished the competition in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a task focused on whole-image classification. This raised an electrifying question in computer vision: could the incredible power of these deep networks, trained for classifying entire images, be harnessed for the more complex task of detecting and localizing specific objects within an image? ...