Customer-obsessed science

We introduce two models for high precision sound event detection leveraging transfer learning. The sound events we detect include “speech”, “music”, and “chime”. Both models consist of a CNN backbone pre-trained using AudioSet for audio classification. To get high precision detection results, the first model employs transposed convolutional layers as the detection head, while the second model uses Feature

The online advertising industry heavily relies on auction mechanisms to allocate impression opportunities to advertisers, and price them. In real-world auctions, we want to maximise welfare for auction participants while being incentive-compatible (allowing bidders to bid truthfully), but we may also wish to include other constraints such as improving revenue for the seller, or mitigating externalities

The demand for accurate estimation of marketing’s incremental effect is rapidly increasing to enable marketers to make informed decisions on their ad investment. The process of ad-mapping links an ad shown to consumers on the fixed marketing channels (Linear TV, Digital, Social) to a marketing creative video. Thus, an accurate ad-mapping, which is a special case of video copy detection, is a cornerstone

Few-shot learning has attracted significant scientific interest in the past decade due to its applicability to visual tasks with a natural long-tailed distribution such as object detection. This paper introduces a novel and flexible few-shot object detection approach which can be adapted effortlessly to any candidate-based object detection framework. In particular, our proposed kFEW component leverages

Detecting commercial Ads from a video is important. For example, the commercial break frequency and duration are two metrics to measure the user experience for streaming service providers such as Amazon Freevee. The detection can be done intrusively by intercepting the network traffic and then parsing the service providers data and logs, or non-intrusively by capturing the videos streamed by content providers