When the development team at Infinity Ward rolled out the massive playing area of Verdansk for "Call of Duty: Warzone," they viewed it as a starting point. After what game director at Infinity Ward Jack O'Hara describes as a short break, they turned their attention to building their next map -- Al Mazrah, the sprawling environment that is Call of Duty's biggest battle royale map to date, and serves as the battleground for "Warzone 2.0," which releases Nov. 16. "We started on this map straight after Verdansk," O'Hara said. "We kind of rolled from that one to a little bit of a breather and then we started laying the foundations for the next map, which is Al Mazrah. It's a chance to refine what we did last time and a chance to build on all the lessons."

Your browser does not support the video element. The hero of the Halo video game franchise, Master Chief, made his debut in 2001 in the game "Halo: Combat Evolved" for the original Xbox game console. In the time since, he has become Microsoft's version of Mario, though just a bit grittier. The Chief, in his iconic green armor, and his fellow superhuman Spartans are more juggernauts than soldiers. Weighing a half-ton, and able to absorb anything short of a tank shell, the armor includes technological enhancements that improve everything from vision to reflexes, turning Spartans into the ultimate weapons.

In this post, I will show you how to implement virtual background replacement. We will be using the WebRTC APIs to capture a stream from a camera, and then employ (better word here?) Machine Learning with MediaPipe and WebAssembly to remove the background. MediaPipe is a cross-platform Machine Learning solution for live and streaming media (should this be live and streaming?) Background replacement is just one of many features it has.

Video question answering requires models to understand and reason about both complex video and language data to correctly derive answers. Existing efforts focus on designing sophisticated cross-modal interactions to fuse the information from two modalities, while encoding the video and question holistically as frame and word sequences. Despite their success, these methods are essentially revolving around the sequential nature of video- and question-contents, providing little insight to the problem of question-answering and lacking interpretability as well. In this work, we argue that while video is presented in frame sequence, the visual elements (eg, objects, actions, activities and events) are not sequential but rather hierarchical in semantic space. To align with the multi-granular essence of linguistic concepts in language queries, we propose to model video as a conditional graph hierarchy which weaves together visual facts of different granularity in a level-wise manner, with the guidance of corresponding textual cues. Despite the simplicity, our extensive experiments demonstrate the superiority of such conditional hierarchical graph architecture, with clear performance improvements over prior methods and also better generalization across different type of questions. Further analyses also consolidate the model's reliability as it shows meaningful visual-textual evidences for the predicted answers.

In recent years, the world has seen many major breakthroughs in the field of Machine Learning and so did the libraries used to write these programs. From NumPy to Tensorflow.js, each one faster and equipped with the best available technology of time than the previous one. One such library that we are going to talk about today is ml5.js. I recently started writing Deep Learning apps for browsers and can tell you this is pretty much fun and easy when you want to showcase your work to the general public or create an MVP in a hackathon with a cool dashboard and good-looking UI elements. I started programming on the web mainly because of the fact one can easily create an extensive user experience using just CSS and HTML5.