Explore the concept of liquid neural networks, how they compare to traditional neural networks, and their applications.

The Xception model is a Computer vision model that utilizes depthwise separable convolution and is a successor of Inception architecture.

Neural Radiance Fields (NeRFs) are a deep learning technique that is revolutionizing the way we represent and interact with 3D scenes. Discover the core concepts behind NeRFs novel view synthesis, learn about cutting-edge variations, explore their applications and a code example.

GoogLeNet is an Image Classification model that is made by stacking Inception Modules. Released in 2014, it surpassed previous benchmarks.

Spatio-Temporal Action Recognition encompasses the complex task of understanding actions in videos, including what action is happening and when and where it occurs.

Explore the Definition and Scope of Promptable Object Detection (POD) and discover how POD integrates natural language prompts with object detection systems, transforming traditional frameworks.

Explore the intricacies of Panoptic Segmentation, its principles, datasets, applications, and future in our comprehensive guide.

Feature extraction transforms raw data into representative feature sets that capture relevant information for a particular task or analysis.