class7

03/04/2020 – Dylan Finch – Pull the Plug?

Dylan Finch 1 Comment

Word count: 596

Summary of the Reading

The main goal of this paper is to make image segmentation more efficient. Image segmentation as it is now, requires humans to help with the process. there are just some images that machines cannot segment on their own. However, there are many cases where an image segmentation algorithm can do all of the work on its own. This presents a problem: we do not know when we can use an algorithm and when we have to use a human, so we have to have humans review all of the segmentations. This is highly inefficient. This paper tries to solve this problem by introducing an algorithm that can decide when a human is required to segment an image. The process described in the paper involves scoring each segmented image done by machines, then giving humans the task of reviewing the lowest scoring images. Overall, the process was very effective and saved a lot of human effort.

Reflections and Connections

I think that this paper gives a great example of how humans and machines should interact, especially when it comes to humans and AIs interacting. Often times, we set out in research with the goal of creating a completely automated process that throws the human away and tries to create an AI or some other kind of machine that will do all of the work. This is often a very bad solution. AIs as they currently are, are not good enough to do most complex tasks all by themselves. In the cases of tasks like image segmentation, this is an especially big issue. These tasks are very easy for humans to do and very hard for AIs to do. So, it is good to see a researcher who is willing to use human strengths to make up for the weaknesses of machines. I think it is a good thing to have both things working together.

This paper also gives us some very important research, trying to answer the question of when we should machines and when we should use humans. This is a very tough question and it comes up in a lot of different fields. Humans are expensive, but machines are often imperfect. It can be very hard to decide when you should use one or the other. This paper does a great job of answering this question for image segmentation and I would love to see more similar research in other fields explain when it is best to use humans and machines in those fields. 

While I like this paper, I do also worry that it is simply moving the problem, rather than actually solving it. Now, instead of needing to improve a segmentation algorithm, we need to improve the scoring algorithm for the segmentations. Have we really improved the solution or have we just moved the area that now needs further improvement? 

Questions

  1. How could this kind of technology be used in other fields? How can we more efficiently use human and machine strengths together?
  2. In general, when do you think it is appropriate to create a system like this? When should we not fully rely on AI or machines?
  3. Did this paper just move the problem, or do you think that this method is better than just creating a better image segmentation algorithm? 
  4. Does creating systems like this stifle innovation on the main problem?
  5. Do you think machines will one day be good enough to segment images with no human input? How far off do you think that is?

Read More

3/4/20 – Jooyoung Whang – Toward Scalable Social Alt Text: Conversational Crowdsourcing as a Tool for Refining Vision-to-Language Technology for the Blind

Jooyoung Whang 1 Comment

In this paper, the authors study the effectiveness of vision-to-language systems for automatically generating alt texts for images and the impact of human-in-the-loop for this task. The authors set up four methods for generating alt text. First is a simple implementation of modern vision-to-language alt text generation. The second is a human-adjusted version of the first method. The third method is a more involved one, where a Blind or Vision Impaired (BVI) user chats with a non-BVI user to gain more context about an image. The final method is a generalized version of the third method, where the authors analyzed the patterns of questions asked during the third method to form a structured set of pre-defined questions that a crowdsource worker can directly provide the answer to without having the need for a lengthy conversation. The authors conclude that current vision-to-language techniques can, in fact, harm context understanding for BVI users, and simple human-in-the-loop methods significantly outperform. They also found that the method of the structured questions worked the best.

This was an interesting study that implicitly pointed out the limitation of computers at understanding social context which is a human affordance. The authors stated that the results of a vision-to-language system often confused the users because the system did not get the point. This made me wonder if the current limitation could be overcome in the future.

I was also concerned whether the authors’ proposed methods were even practical. Sure, the human-in-the-loop method involving Mturk workers greatly enhanced the description of a Twitter image, but based on their report, it’ll take too long to retrieve the description. The paper reports that to answer one of the structured questions, it takes on average, 1 minute. This is excluding the time it takes for a Mturk worker to accept a HIT. The authors suggested pre-generating alt texts for popular Tweets, but this does not completely solve the problem.

I was also skeptical about the way the authors performed validation with the 7 BVI users. In their validation, they simulated their third method (TweetTalk, a conversation between BVI and sighted users). However, they did not do it by using their application, but rather a face-to-face conversation between the researchers and the participants. The authors claimed that they tried to replicate the environment as much as possible, but I think there still can be flaws since the researchers serving as the sighted user already had expert knowledge about their experiment. Also, as stated in the paper’s limitations section, the validation was performed with too fewer participants. This may not fully capture the BVI users’ behaviors.

These are the questions that I had while reading this paper:

1. Do you think the authors’ proposed methods are actually practical? What could be done to make them practical if you don’t think so?

2. What do you think were the human affordances needed for the human element of this experiment other than social awareness?

3. Do you think the authors’ validation with the BVI users is sound? Also, the validation was only done for the third method. How can the validation be done for the rest of the methods?

Read More

03/04/2020 – Vikram Mohanty – Combining crowdsourcing and google street view to identify street-level accessibility problems

Vikram Mohanty Leave a comment

Authors: Kotaro Hara, Vicki Le, and Jon Froehlich

Summary

This paper discusses the feasibility of using AMT crowd workers to label sidewalk accessibility problems in Google Street View. The authors create ground truth datasets with the help of wheelchair users, and found that Turkers reached an accuracy of 81%. The paper also discusses some quality control and improvement methods, which was shown to be effective i.e. improved the accuracy to 93%. 

Reflection

This paper reminded me of Jeff Bigham’s quote – “Discovery of important problems, mapping them onto computationally tractable solutions, collecting meaningful datasets, and designing interactions that make sense to people is where HCI and its inherent methodologies shine.” It’s a great example for two important things mentioned in the quote : a) discovery of important problems, and b) collecting meaningful datasets. The paper’s contribution mentions that the datasets collected will be used for building computer vision algorithms, and this paper’s workflow involves the potential end-users (wheelchair users) early on in the process. Further, the paper attempts to use Turkers to generate datasets that are comparable in quality to that of the wheelchair users, essentially setting a high quality standard for generating potential AI datasets. This is a desirable approach for training datasets, which can potentially help prevent problems in popular datasets as outlined here: https://www.excavating.ai/. 

The paper also proposed two generalizable methods for improving data quality from Turkers. Filtering out low-quality workers during data collection by seeding in gold standard data may require designing modular workflows, but the time investment may well be worth it. 

It’s great to see how this work evolved to now form the basis for Project Sidewalk, a live project where volunteers can map accessibility areas in the neighborhood.

Questions

  1. What’s your usual process for gathering datasets? How is it different from this paper’s approach? Would you be willing to involve potential end-users in the process? 
  2. What would you do to ensure quality control in your AMT tasks? 
  3. Do you think collecting more fine-grained data for training CV algorithms will come at a trade-off for the interface not being simple enough for Turkers?

Read More

03/04/2020 – Nurendra Choudhary – Real-time captioning by groups of non-experts

Nurendra Choudhary 1 Comment

Summary

In this paper, the authors discuss a collaborative real-time captioning framework called LEGION:SCRIBE. They compare their system against the previous approach called CART and Automated Speech Recognition (ASR) system. The authors initiate the discussion with the benefits of captioning. They proceed to explain the expensive cost of hiring stenographers. Stenographers are the fastest and most accurate captioners with access to specialized keyboards and expertise in the area. However, they are prohibitively expensive (100-120$ an hour). ASR is much cheaper but their low accuracy deems them inapplicable in most real-world scenarios. 

To alleviate the issues, the authors introduce SCRIBE framework. In SCRIBE, crowd-workers caption smaller parts of the speech. The parts are merged using an independent framework to form the final sentence. The latency of the system is 2.89s, emphasizing its real-time nature, which is a significant improvement over ~5s of CART.

Reflection

The paper introduces an interesting approach to collate data from multiple crowd workers for sequence learning tasks. The method has been applied before in cases such as Google Translate (translating small phrases) and ASR (voice recognition of speech segments). However, SCRIBE distinguishes itself by bringing in real-time improvement in the system. But, the system relies on the availability of crowd workers. This may lead to unreliable behaviour in the system. Additionally, the hired workers are not professionals. Hence, the quality is affected by human behavioral features such as mindset, emotions or mental stamina. I believe a study on the evolution of SCRIBE overtime and its dependence on such features needs to be analyzed.

Furthermore, I question the crowd management system. Amazon MT cannot guarantee real-time labourers. Currently, given the supply of workers with respect to the tasks, workers are always available. However, as more users adopt the system, this need not always hold true. So, crowd management systems should provide alternatives that guarantee such requirements. Also, the work provider needs to find alternatives to maintain real-time interaction, in case the crowd fails. In case of SCRIBE, the authors can append an ASR module in a situation of crowd failure. ASR may not give the best results but would be able to ensure smoother user experience.

The current development system does not consider the volatility of crowd management systems. This makes them an external single point of failure. I think there should be a push in the direction of simultaneously adopting multiple management systems for the framework to increase their reliability. This will also improve system efficiency because it has a more diverse set of results as choice. Thus benefiting the overall model structure and user adoption. 

Questions

  1. Google Translate uses a similar strategy by asking its users to translate parts of sentences. Can this technique be globally applied to any sequential learning framework? Is there a way we can divide sequences into independent segments? In case of dependent segments, can we just use a similar merging module or is it always problem-dependent?
  2. The system depends on the availability of crowd workers. Should there be a study on the availability aspect? What kind of systems would be benefitted from this?
  3. Should there be a new crowd work management system with a sole focus on providing real-time data provisions?
  4. Should the responsibility of ensuring real-time nature be on the management system or the work provider? How will it impact the current development framework?

Word Count: 567

Read More

03/04/2020 – Toward Scalable Social Alt Text: Conversational Crowdsourcing as a Tool for Refining Vision-to-Language Technology for the Blind – Yuhang Liu

yuhang Liu 1 Comment

Summary:

The authors of this paper explored that visually impaired users are limited by the availability of suitable alternative text when accessing images in social media. The author believes that the beneficial of those new tools that can automatically generate captions are unknown to the blind. So through experiments, the authors studied how to use crowdsourcing to evaluate the value provided by existing automation methods, and how to provide a scalable and useful alternative text workflow for blind users. Using real-time crowdsourcing, the authors designed crowd-interaction experiments that can change the depth. These experiments can help explain the shortcomings of existing methods. The experiments show that the shortcomings of existing AI image captioning systems often prevent users from understanding the images they cannot see , And even some conversations can produce erroneous results, which greatly affect the user experience. The authors carried out a detailed analysis and designed a design that is scalable, requires crowdsourced workers to participate in improving the display content, and can effectively help users without real-time interaction.

Reflection:

First of all, I very much agree with the author’s approach. In a society where the role of social networks is increasingly important, we really should strive to make social media serve more people, especially for the disadvantaged groups in our lives. The blind daliy travel inconveniently, social media is their main way to understand the world, so designing such a system would be a very good idea if it can help them. Secondly, the author used the crowdsourcing method to study the existing methods. The method they designed is also very effective. As a cheap human resource, the crowdsourcing method can test a large number of systems in a short time, but I think this method There are also some limitations. It may be difficult for these crowdsourced workers to think about the problem from the perspective of the blind, which makes their ideas, although similar to the blind, not very accurate, so there are some gaps of the results with blind users. Finally, I have some doubts about the system proposed by the author. The authors finally proposed a workflow that combines different levels of automation and human participation. This shows that this interaction requires the participation of another person, so I think this interaction There are some disadvantages to this method. Not only will it cause a certain delay, but because it requires other human resources, it also requires some blind users to pay more. I think the ultimate direction of development should be free from human constraints, so I think we can compare the results of workers with the original results and let machine learning. That is to use the results of crowdsourcing workers for machine learning. I think it can reduce the cost of the system while increasing the efficiency of the system, and provide faster and better services for more blind users.

Question：

  1. Do you think there is a better way to implement these functions, such as studying the answers of workers, and achieving a completely automatic display system?
  2. Are there some disadvantages to using crowdsourcing platforms?
  3. Is it better to change text to speech for the visually impaired?

Read More

03/04/20 – Lulwah AlKulaib- CrowdStreetView

Lulwah AlKulaib 1 Comment

Summary

The authors try to assess the accessibility of sidewalks by hiring AMT workers to analyze Google Street View images. Traditionally, sidewalk assessment is conducted in person via street audits which are  highly labor intensive and expensive or by reporting calls from citizens. The authors propose using their system as an alternative for a proactive solution to this issue. They perform two studies:

  • A feasibility study (Study 1): examines the feasibility of the labeling task with six dedicated labelers including three wheelchair users
  • A crowdsourcing study (Study 2): investigates the comparative performance of turkers

In study 1, since labeling sidewalk accessibility problems is subjective and potentially ambiguous, the authors investigate the viability of labeling across two groups:

  • Three members of the research team
  • Three wheelchair users – accessibility experts

They use the results of study 1 to provide ground truth labels to evaluate crowdworkers performance and to get a baseline understanding of what labeling this dataset looks like. In study 2, the authors investigate the potential of using crowd workers to perform the labeling task. They evaluate their performance on two levels of labeling accuracy:

  • Image level: tests for the presence or absence of the correct label in an image 
  • Pixel level: examines the pixel level accuracies of the provided labels

They show that AMT workers are capable of finding accessibility problems with an accuracy of 80.6 % and determining the correct problem type with an accuracy of 78.3%. They get better results when using majority voting as a labeling technique 86.9% and 83.9% respectively. They collected 13,379 labels, 19,189verification  labels from 402 workers. Their findings suggest that crowdsourcing both the labeling task and the verification task leads to a better quality result.

Reflection

The authors have selected experts in the paper as wheelchair users, when in real life they’re civil engineers. I wonder how that would have changed their labels/results. Since accessibility in the street is not only for wheelchair users. It’s worth investigating by using a pool of multiple experts. 

I also think that selecting the dataset of photos to work on was a requirement for this labeling system, else it would have been tedious amount of work on “bad” images. I can’t imagine how this would be a scalable system on google street view as a whole. The dataset requires refinement to be able to label.

In addition, the focal point of the camera was not considered and reduces the scalability of the project. Even though the authors suggest a solution of installing a camera angled towards sidewalks, until that is implemented, I don’t see how this model could work well in the real world (not a controlled experiment).

Discussion

  • What are improvements that the authors could have done to their analysis?
  • How would their labeling system work for random Google street view photos?
  • How would the focal point of the GSV camera affect the labeling? 
  • If cameras were angled towards sidewalks, and we were able to get a huge amount of photos for analysis, what would be a good way to implement this project?

Read More

3/4/20 – Jooyoung Whang – Pull the Plug? Predicting If Computers or Humans Should Segment Images

Jooyoung Whang Leave a comment

In this paper, the authors attempt to appropriately distribute human and computer resources for creating segmentation of foreground objects in an image to achieve highly precise segmentations. They introduce that the segmentation process consists of roughly segmenting the image (initialization), and then going through another fine-grained iteration to come up with the final result. They repeat their study for both of the steps. To figure out where to allocate human resources, the authors’ proposed an algorithm that tries to score the acquired segmentations by detecting: highly jagged edges on the boundary, non-compact segmentations, near-edge segmentation locations, and segmentation area ratio to the full image. The authors find that a mix of humans and computers for image segmentation performs better than when completely using one or the other.

I liked the authors’ proposed algorithm to detect when a segmentation fails. It was interesting to see that they focused on visible features and qualities that humans can see instead of relying on deep neural networks that are often hard to interpret the internal workings of. At the same time, I am a little concerned about whether the proposed visual features for failed segmentations are enough to generalize and scale for all kinds of images. For example, the authors note that failed segmentations often have highly jagged edges. What if the foreground object (or an animal in this case) was a porcupine? The score would be fairly low even when an algorithm correctly segments the creature from the background. Of course, the paper reports that the method generalized well for everyday images and biomedical images, so my concern may be a trivial one.

As I am not experienced in the field of image segmentation analysis, I wondered if there were any case where an image contained more than one foreground objects and only one of them is of interest to a researcher. From my short knowledge about fore and background separation, a graph search is done by treating the image as a graph of connected pixels to find pixels that stand out. It does not care about “objects of interest.” It made me curious if it was possible to give additional semantic information in the process.

The followings are the questions that I had while reading the paper:

1. Do you think the qualities that PTP looks for is enough to measure the score of the quality of segmented images? What other properties would a failed segmentation have? One quality I can think of is that failed segmentations often have disjoint parts in the segmentations.

2. Can you think of some cases where PTP could fail? Would there be any case where the score for a segmentation score really low even if the segmentation was done correctly?

3. As I’ve written in my reflection, are there methods that allow segmentation algorithms to consider the “interest” for an object? For example, if an image contained a car and a cat both in the foreground and the researcher was interested in the cat, would the algorithm be able to only separate out the cat?

Read More

03/04/20 – Lulwah AlKulaib- SocialAltText

Lulwah AlKulaib Leave a comment

Summary

The authors propose a system to generate Alt text for images embedded in social media posts by utilizing crowd workers. Their goal is to have a better experience for the blind and visually impared (BVI) when using social media. Existing tools provide imperfect descriptions some by automatic caption generation, and others by object recognition. These systems are not enough as in many cases their results aren’t descriptive enough for BVI users. The authors study how crowdsourcing can be used for both:

  • evaluating the value provided of existing automated approaches
  • Enabling workflows that provide scalable and useful alt text for BVI users

They utilize real-time crowdsourcing to test experiments with varied depth levels of interaction of the crowd in assisting visually impaired users. They show the shortcomings of existing AI image captioning systems and compare them with their method. The paper suggests two experiences:

  • TweetTalk: is a conversational assistant workflow.
  • Structured Q&A: that builds upon and enhances the state of the art generated captions.

They evaluated the conversational assistant with 235 crowdworkers. They evaluated 85 tweets for the baseline image caption, each tweet was evaluated 3 times with a total of 255 evaluations.

Reflection

The paper presents a novel concept and their approach is a different take on utilizing crowdworkers. I believe that the experiment would have worked better if they tested it on some visually impared users. Since the crowdworkers hired were not visually impaired, it makes it harder to say that BVI users would have the same reaction. Since the study targets BVI users, they should have been the pool of testers. People interact with the same element in different ways and what they showed seemed too controlled. Also, the questions were not all the same for all images, which makes this harder to generalize. The presented model tries to solve a problem for social media photos and not having a plan to repeat for each photo might make interpreting images difficult. 

I appreciated the authors’ use of existing systems and their attempt at improving the AI generated captions. Their results obtain better accuracy compared to state of the art work.

I would have loved seeing how different social media applications measured compared with each other. Since different applications vary in how they present photos. Twitter for example gives a limited amount of character count while Facebook could present more text which might help BVI users in understanding the image better. 

In the limitations section, the authors mention that human in the loop workflows raise privacy concerns and that the alt text would generalize to friendsourcing and utilizing social network users. I wonder how that generalizes to social media applications in real time. And how reliable would friendsourcing be for BVI users.

Discussion

  • What are improvements that you would suggest to better the TweetTalk experiment?
  • Do you know of any applications that use human in the loop in real time?
  • Would you have any privacy concerns if one of the social media applications integrated a human in the loop approach to help BVI users?

Read More

03/04/2020 – Ziyao Wang – Combining crowdsourcing and google street view to identify street-level accessibility problems

Ziyao Wang Leave a comment

In this paper, the authors focused on the mechanism that using untrained crowdworkers to find and label accessibility problems in Google Street View imagery. They provide the workers images from Google Street View imagery to let them find, label and access sidewalk accessibility problems. They compared the results of this labeling task completed by six dedicated labelers including three wheelchair users and by MTurk workers. The comparison shows that the crowdworkers can determining the presence of an accessibility problem with high accuracy, which means this mechanism is promising about sidewalk accessibility. However, that mechanism still have problems such as locating the GSV camera in geographic space and selecting an optimal viewpoint, sidewalk width problem and the age of the images. In the experiments, the workers cannot label some of the images due to camera position, and the images may be captured three years ago. Additionally, there is no method to measure the width of the sidewalk, which is a need by the wheelchair users.

Reflections:

The authors combined the Google Street View imagery and MTurk Crowdsourcing to build a system which can detect accessibility challenges. This kind of hybrid system has a high accuracy in the finding and labeling of such kind of accessibility challenges. If this system can be used practically, the disables will benefit a lot with the help of the system.

However, there is some problems in the system. As is mentioned in the paper, the images in the Google Street View are old. Some of the images may be captured years ago. If the detection is based on these pictures, some new access problems will be detected. For this problem, I have a rough idea about letting the users of the system to update the image library. When they found some difference between the images from library and practical sidewalk, they can upload the latest pictures captured by them. As a result, other users will not suffer from the images’ age problem. However, this solution will change the whole system. Google Street View imagery requires professional capture devices which is not available to most of the users. As a result, the Google Street View will not update its imagery using the photos captured by the users, and the system cannot update itself using the imagery. Instead, the system has to build its own image library, which is totally different from the introduced system in the paper. Additionally, the photos provided by the users may be with low resolution, and it will be difficult for the MTurk workers to label the accessibility challenges.

Similarly, the problem that the workers cannot measure the width of the sidewalk can be solved if users can upload the width when they are using the system. However, it still faces the problem of lacking an own database and the system needs to be modified hugely.

Instead of detecting accessibility challenges, I think the system is more useful in tracking and labeling bike lanes. Compared with the accessibility of sidewalk, to detect the existence of bike lanes will suffer less from the age problem, because even the bike lanes were built years ago, they can still work. Also, there is no need to measure the width of the lanes, as all the lanes should have enough space for bikes to pass.

Question:

Is there any approach to solve the age problem, camera point problem and measuring width problem in the system?

What do you think about applying such a system to track and label bike lanes?

What other kinds of street detection problems can this system being applied to?

Read More

03/04/2020- Ziyao Wang – Real-time captioning by groups of non-experts

Ziyao Wang 1 Comment

Traditional real-time captioning tasks are completed by professional captionists. However, the cost to hire them is expensive. Alternatively, some automatic speech recognition systems have been developed. But there is still problem that these systems perform badly when the audio quality is low or there are multiple people talking. In this paper, the authors developed a system which can hire several non-expert workers to do the caption task and merge their works together to obtain a high accuracy caption output. As the workers have a significant lower salary compared with the experts, the cost will be reduced even multiple workers are hired. Also, the system has a good performance collecting workers’ jobs and merging them to get a high accuracy output with low latency.

Reflections:

When solving problems with the requirement of high accuracy and low latency, I always hold the view that only AI or experts can complete such kind of tasks. However, in this paper, the authors showed us that non-experts can also complete this kind of tasks if we can have a group of people work together.

Compared with the professionals, hiring non-experts will cost much less. Compared with AI, people can handle some complicated situations better. This system combined this two advantages and provided a cheap real-time captioning system with high accuracy.

It is for sure that this system has lots of advantages, but we should still consider it critically. For the cost, it is true that hiring non-experts will spend much less than hiring professional captionists. However, the system needs to hire 10 workers to get 80 to 90 percentage accuracy. Even though the workers have a low salary, for example 10 dollars per hour, the total cost will reach 100 dollars per hour. Hiring experts will only cost around 120 dollars for one hour, which shows that the saving of applying the system is relatively low.

For the accuracy part, there is possibility that all the 10 workers missed a part of the audio. As a result, even merging all the results provided by the workers, the system will still miss this part’s caption. Instead, though the AI system may provide caption with errors, the system can at least provide something for all words in the audio.

For these two reasons, I think hiring less workers, for example three to five workers, to fix the errors in the system generated caption will save more money while the system can still maintain high accuracy. And with the provided caption, the workers’ tasks will be easier, and they may provide more accurate results. Also, for the circumstances in which AI system performs well, the workers will not need to spent time typing, and the latency of the system will be reduced.

Questions:

What are the advantages of hiring non-expert humans to do the captioning compared with the experts or AI systems?

Will a system hiring less workers to fix the errors in the AI generated caption be cheaper? Will this system perform better?

For the system mentioned in the second question, does it have any limitations or drawbacks?

Read More