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Available Internship Positions: Sales Academy Intern, Graphic Design Intern, Summer Intern, High School Intern, Software Intern, Machine Learning Intern, MBA Intern, Student Intern.
Apply Online Adobe Intern Program
Adobe Research is currently offering internships. To apply, please contact us with your CV, a list of your research interests, and any specific researchers you would like to work with.
Internships at Adobe Research are an excellent way for graduate students pursuing a Ph.D. to engage in industrial research. Interns work on exciting technology that is routinely published at top academic conferences and integrated into software that impacts the lives of millions of customers.
Smart Geo-fencing Based on Geo-distribution of Mobile User Activity
Geo-fencing is a location-based service that allows marketers to send messages to smartphone users who enter a predefined geographical area. This helps them to target the foot traffic in the vicinity of a point-of-interest. Current geo-fencing technology creates static geo-fences and thus is oblivious to users’ mobile app behavior. This results in suboptimal targeting of potential customers with respect to when, where and what messages are triggered.
This project aims to assist marketers in creating smarter geo-fences. Our algorithm automatically segments users based on their geo-distributions of mobile app activity, identifies points-of-interest and then suggests creation of geo-fences customized to each user segment. The geo-fences can be specific to a product or service and is parameterized by a threshold to balance between effectiveness and unwanted targeting. Marketing messages can be personalized to each user segment and can be auto-triggered on entry/exit from a geo-fence. Our data-driven geo-fencing algorithm is less prone to human biases and can sometimes capture surprising aggregate mobile app behavior in the form of geo-fences around unexpected points-of-interest.
We have developed interactive image editing tools using a new randomized algorithm for quickly finding approximate nearest neighbor matches between image patches. Previous research in graphics and vision has leveraged such nearest-neighbor searches to provide a variety of high-level digital image editing tools. However, the cost of computing a field of such matches for an entire image has eluded previous efforts to provide interactive performance. Our algorithm offers substantial performance improvements over the previous state of the art (20-100x), enabling its use in interactive editing tools. The key insights driving the algorithm are that some good patch matches can be found via random sampling, and that natural coherence in the imagery allows us to propagate such matches quickly to surrounding areas. We offer theoretical analysis of the convergence properties of the algorithm, as well as empirical and practical evidence for its high quality and performance. This one simple algorithm forms the basis for a variety of tools – image retargeting, completion and reshuffling – that can be used together in the context of a high-level image editing application. Finally, we propose additional intuitive constraints on the synthesis process that offer the user a level of control unavailable in previous methods.
PatchMatch was later generalized and shown to be useful for a variety of computer vision applications like image denoising, object detection, label transfer and more.The patch synthesis capabilities shown here were later enhanced in the Image Melding project. Some of our later citations using PatchMatch as a building block can be found below.
PatchMatch is part of the Content Aware Fill feature in Photoshop CS5, the Content Aware Patch and Move tools in Photoshop CS6 (with further improvements in later versions). It was also used as a core algorithm in the Video Tapestries, Cosaliency, Regenerative Morphing and Non-Rigid Dense Correspondence research projects.
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