Summary
Advanced graphics designs such as visual blending are a very difficult art to master. Let alone for a singular person to understand and able to create them. Utilizing different designs through various companies to ensure the widest market share and the greatest public opinion requires time, effort, and typically a team of experts to ensure quality. Aiming to ensure a lower barrier of entry for younger or smaller companies, this team created VisiBlends, a crowd sourced framework aiming to ease the burden on teams. This framework breaks down the task of visual blends into 6 different stages to ensure there is validation throughout the whole framework. Throughout the various studies the team had created to measure the results, there was a majority success stapling this usage as a potential tool to generate visual blend assistance through the whole process.
Reflection
I think this problem domain is similar to the research problem domain in various aspects. Researchers look at the bleeding edge of technology and aim to solve new or old problems using new techniques. These techniques are usually accumulated through various studies, various other methods, or even a new method that uses existing technology to extend previous work. It is interesting to see how the process for creating a new research idea can also be applied throughout the 6 different steps within this teams framework, however with the lab mates, advisor, and committee filling in for the users being crowd sourced within the original framework.
I do appreciate the machine learning algorithm learning to match the two different pictures (or ideas). This provides several starting places for uses while continuously learning the better matching locations. I believe this could be improved by allowing a human in the loop factor or an override however. Within this kind of work the art is continuously being improved upon in a real time scale. To keep up design artists may have a better idea of new ideas to be used and could be used as an early wave, better feeding the algorithm with new results.
Questions
- Visual blend problems are potentially a factor when marketing for a new item or product. Blending two (and potentially very different) objects helps to grab the attention of passersby who only give it a split second while either scrolling past the story or going past the ad. Have you had any experience in creating a visual blend for one of your products and did you use this technique to reach a specific demographic?
- One of the perceived problems throughout Visual Blends is the specific pairing of elements that can be related to each other. Notably within this study this was overcome by obtaining the consensus (throughout the Mechanical Turk Workers) the initial idea that came to their head with the idea. Do you think this could be alleviated by using another Machine Learning algorithm pairing with the stream of information from a social media website (accounting for the social consensus of a group)?
- This technique could potentially remove part of the need for specific marketing teams who specialize in visual blending. Do you think this program could become widespread or integrated with another bigger system already in use by another company?
I would like to comment on your third question. I also believe the technique could potentially remove the need for various positions. However, at the same time it is also going to create various new positions for the user input stages. Although the actual task will be algorithmically done, however, the searching and finding of appropriate images can only be done by humans. Additionally, if the task is extended to complex subjects, some domain experts might be needed to annotate the data properly. I’m not sure which companies exactly use them currently, but like all tasks nowadays, more and more automations are bound to happen and replace humans in their jobs. However, nonetheless as we discussed in the class repeatedly, machines are nowhere near replacing humans and some aspects of creativity, intelligence of humans are irreplaceable.