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: Service (business) & Service provider. The organization has 13363 authors who have published 17317 publications receiving 266589 citations.

Speech recognition capability generation and control

Abstract: A system for controlling multiple devices using automatic speech recognition (ASR) even when the devices may not be capable of performing ASR themselves A device such as a media player, appliance, or the like may be recognized by a network The configured controls for the device (such as a remote control or other mechanism) are incorporated into a device control registry which catalogs device command controls Individual ASR grammars are constructed for the devices so speech commands for those devices may be processed by an ASR device The ASR device may then process those speech commands and convert them into the appropriate inputs for the controlled device The inputs may then be sent to the controlled device, resulting in ASR control for non-ASR devices

The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research

Abstract: Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA Challenges are an established and important way to drive scientific progress. They make research comparable on a well-defined benchmark with equal test conditions for all participants. However, such challenge events occur only occasionally, are limited to a small number of contestants, and the test conditions are very difficult to replicate after the main event. We present a new physical benchmark challenge for robotic picking: the ACRV Picking Benchmark. Designed to be reproducible, it consists of a set of 42 common objects, a widely available shelf, and exact guidelines for object arrangement using stencils. A well-defined evaluation protocol enables the comparison of complete robotic systems — including perception and manipulation — instead of sub-systems only. Our paper also describes and reports results achieved by an open baseline system based on a Baxter robot.

GANs for Biological Image Synthesis

Abstract: In this paper, we propose a novel application of Generative Adversarial Networks (GAN) to the synthesis of cells imaged by fluorescence microscopy. Compared to natural images, cells tend to have a simpler and more geometric global structure that facilitates image generation. However, the correlation between the spatial pattern of different fluorescent proteins reflects important biological functions, and synthesized images have to capture these relationships to be relevant for biological applications. We adapt GANs to the task at hand and propose new models with casual dependencies between image channels that can generate multichannel images, which would be impossible to obtain experimentally. We evaluate our approach using two independent techniques and compare it against sensible baselines. Finally, we demonstrate that by interpolating across the latent space we can mimic the known changes in protein localization that occur through time during the cell cycle, allowing us to predict temporal evolution from static images.

Image Search With Text Feedback by Visiolinguistic Attention Learning

Abstract: Image search with text feedback has promising impacts in various real-world applications, such as e-commerce and internet search. Given a reference image and text feedback from user, the goal is to retrieve images that not only resemble the input image, but also change certain aspects in accordance with the given text. This is a challenging task as it requires the synergistic understanding of both image and text. In this work, we tackle this task by a novel Visiolinguistic Attention Learning (VAL) framework. Specifically, we propose a composite transformer that can be seamlessly plugged in a CNN to selectively preserve and transform the visual features conditioned on language semantics. By inserting multiple composite transformers at varying depths, VAL is incentive to encapsulate the multi-granular visiolinguistic information, thus yielding an expressive representation for effective image search. We conduct comprehensive evaluation on three datasets: Fashion200k, Shoes and FashionIQ. Extensive experiments show our model exceeds existing approaches on all datasets, demonstrating consistent superiority in coping with various text feedbacks, including attribute-like and natural language descriptions.

Method and system for information exchange between users of different web pages

Abstract: A method and system for allowing users of different web pages to exchange information. The information exchange system identifies groups of related web pages and maintains a database of user-supplied information for each group of related web pages. When a user accesses a web page the information exchange often displays in a separate area the information associated with the group of related web pages. Also the information exchange system allows the user to enter information that will be displayed to other users who access related web pages.