Customer-obsessed science

At E-Commerce stores such as Amazon, eBay, and Taobao, the shopping items and the query words that customers use to search for the items form a bipartite graph that captures search behavior. Such a query-item graph can be used to forecast search trends or improve search results. For example, generating query-item associations, which is equivalent to predicting links in the bipartite graph, can yield recommendations

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

Directed graphs are ubiquitous and have applications across multiple domains including citation, website, social, and traffic networks. Yet, the majority of research involving graph neural networks (GNNs) focus on undirected graphs. In this paper, we deal with the problem of node recommendation in non-attributed directed graphs. Specifically, given a directed graph and query node as input, the goal is to

Accurate per-pixel semantic class annotations of the entire video are crucial for designing and evaluating video semantic segmentation algorithms. However, the annotations are usually limited to a small subset of the video frames due to the high annotation cost and limited budget in practice. In this paper, we propose a novel human-in-the-loop framework called HVSA to generate semantic segmentation annotations