Amazon.com

About: Amazon.com is a company organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Computer science & Service (business). The organization has 13363 authors who have published 17317 publications receiving 266589 citations.

A Comprehensive Study of Deep Video Action Recognition.

Abstract: Video action recognition is one of the representative tasks for video understanding. Over the last decade, we have witnessed great advancements in video action recognition thanks to the emergence of deep learning. But we also encountered new challenges, including modeling long-range temporal information in videos, high computation costs, and incomparable results due to datasets and evaluation protocol variances. In this paper, we provide a comprehensive survey of over 200 existing papers on deep learning for video action recognition. We first introduce the 17 video action recognition datasets that influenced the design of models. Then we present video action recognition models in chronological order: starting with early attempts at adapting deep learning, then to the two-stream networks, followed by the adoption of 3D convolutional kernels, and finally to the recent compute-efficient models. In addition, we benchmark popular methods on several representative datasets and release code for reproducibility. In the end, we discuss open problems and shed light on opportunities for video action recognition to facilitate new research ideas.

Method and apparatus for providing fulfillment services

Abstract: Method and apparatus for providing inventory fulfillment services to customers who have small quantities of heterogeneous items to sell. A self-service registration interface for generating lists of items to sell via the inventory fulfillment service is provided. The inventory fulfillment service may provide pricing suggestions to the customer. The inventory fulfillment service may determine whether a listed item satisfies one or more listing rules. Shipping information for a list of items may be automatically generated and provided to the customer. The customer may ship the items in one shipment to a specified facility. The customer is the seller of record for all items listed. The customer may not be charged for services until an item is sold. A listing period may be specified for which listed items will be carried. If an item does not sell within the period, option(s) for disposal of the item may be provided.

Archival data storage system

Abstract: A cost-effective, durable and scalable archival data storage system is provided herein that allow customers to store, retrieve and delete archival data objects, among other operations. For data storage, in an embodiment, the system stores data in a transient data store and provides a data object identifier may be used by subsequent requests. For data retrieval, in an embodiment, the system creates a job corresponding to the data retrieval and provides a job identifier associated with the created job. Once the job is executed, data retrieved is provided in a transient data store to enable customer download. In various embodiments, jobs associated with storage, retrieval and deletion are scheduled and executed using various optimization techniques such as load balancing, batch processed and partitioning. Data is redundantly encoded and stored in self-describing storage entities increasing reliability while reducing storage costs. Data integrity is ensured by integrity checks along data paths.

Adversarial Domain Adaptation for Duplicate Question Detection

Abstract: We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions. As finding and annotating such potential duplicates manually is very tedious and costly, automatic methods based on machine learning are a viable alternative. However, many forums do not have annotated data, i.e., questions labeled by experts as duplicates, and thus a promising solution is to use domain adaptation from another forum that has such annotations. Here we focus on adversarial domain adaptation, deriving important findings about when it performs well and what properties of the domains are important in this regard. Our experiments with StackExchange data show an average improvement of 5.6% over the best baseline across multiple pairs of domains.