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The contemporary film and media landscape is experiencing a disorienting change driven, in part, by new technology. In film, digital effects and the move to streaming content, has led to a revolution in what is possible visually as well as impacting the economics of film distribution. In the news media,

Species across the globe are experiencing climate change, habitat loss, and poaching, a critical combination of pressures that will lead to mass extinction without effective action. However, protecting threatened wildlife populations is a daunting challenge for governments and NGOs. Lack of resources and expertise mean that environments in need of

Agriculture is being made more efficient and more profitable by a new generation of computer vision based AI applications. These machine learning driven models can analyse crops and livestock and accurately infer a range of information from what they see. The implications of this technology are significant, precision agriculture will

The COVID-19 pandemic has further accelerated the ongoing shift towards internet shopping and home delivery. Delivery giant Amazon reported a near 200% rise in profits in 2020, cementing at home shopping as the dominant consumer habit for the coming decades. As demand for delivery services accelerate computer vision based AI

What is video annotation Many computer vision models are designed to interpret and operate within chaotic, moving, real-world environments. Video annotation helps AI to function effectively in these spaces by applying information to video footage. This information is generally added to videos by human annotators who apply outlines and labels

Defining image annotation Image annotation gives machine learning models information about what a given image is showing. In most cases this information is applied to datasets by human annotators who highlight and label relevant parts of images in accordance with the needs of machine learning developers. Image annotation allows researchers

Three types of image annotation can be used to train your computer vision model: image classification, object detection, and segmentation. Image classification and object detection are fairly straightforward — the former involves labeling the object featured in an image while the latter takes the same process a step further by introducing