Anker's lifestyle brand Eufy has already swallowed a big chunk of the robot vacuum market and now it's got its sights on your yard. The company has been sharing details of its first two robot mowers since the start of the year, and now they're ready to start selling them. Eufy's E15 and E18 are designed to automate one of the most tedious jobs around the home -- if you're able to pay. I've been testing an E15 for the last few weeks ahead of their retail debut today and I'm fairly impressed. Early robot mowers needed a boundary wire to tell them where they were allowed to mow.

In recent years, there have been significant advancements in computer vision which have led to the widespread deployment of image recognition and generation systems in socially relevant applications, from hiring to security screening. However, the prevalence of biases within these systems has raised significant ethical and social concerns. The most extensively studied biases in this context are related to gender, race and age. Yet, other biases are equally pervasive and harmful, such as lookism, i.e., the preferential treatment of individuals based on their physical appearance. Lookism remains under-explored in computer vision but can have profound implications not only by perpetuating harmful societal stereotypes but also by undermining the fairness and inclusivity of AI technologies. Thus, this paper advocates for the systematic study of lookism as a critical bias in computer vision models. Through a comprehensive review of existing literature, we identify three areas of intersection between lookism and computer vision. We illustrate them by means of examples and a user study. We call for an interdisciplinary approach to address lookism, urging researchers, developers, and policymakers to prioritize the development of equitable computer vision systems that respect and reflect the diversity of human appearances.

Laboratory automation is an expensive and complicated endeavor with limited inflexible options for small-scale labs. We develop a prototype system for tending to a bench-top centrifuge using computer vision methods for color detection and circular Hough Transforms to detect and localize centrifuge buckets. Initial results show that the prototype is capable of automating the usage of regular bench-top lab equipment.

Invasive plant species are detrimental to the ecology of both agricultural and wildland areas. Euphorbia esula, or leafy spurge, is one such plant that has spread through much of North America from Eastern Europe. When paired with contemporary computer vision systems, unmanned aerial vehicles, or drones, offer the means to track expansion of problem plants, such as leafy spurge, and improve chances of controlling these weeds. We gathered a dataset of leafy spurge presence and absence in grasslands of western Montana, USA, then surveyed these areas with a commercial drone. We trained image classifiers on these data, and our best performing model, a pre-trained DINOv2 vision transformer, identified leafy spurge with 0.84 accuracy (test set). This result indicates that classification of leafy spurge is tractable, but not solved. We release this unique dataset of labelled and unlabelled, aerial drone imagery for the machine learning community to explore. Improving classification performance of leafy spurge would benefit the fields of ecology, conservation, and remote sensing alike. Code and data are available at our website: leafy-spurge-dataset.github.io.

TL;DR: Off-the-shelf text spotting and re-identification models fail in basic off-road racing settings, even more so during muddy events. Making matters worse, there aren't any public datasets to evaluate or improve models in this domain. To this end, we introduce datasets, benchmarks, and methods for the challenging off-road racing setting. In the dynamic world of sports analytics, machine learning (ML) systems play a pivotal role, transforming vast arrays of visual data into actionable insights. These systems are adept at navigating through thousands of photos to tag athletes, enabling fans and participants alike to swiftly locate images of specific racers or moments from events.

Will artificial intelligence take our jobs? If you listen to Silicon Valley executives talking about the capabilities of today's cutting edge AI systems, you might think the answer is "yes, and soon." But a new paper published by MIT researchers suggests automation in the workforce might happen slower than you think. The researchers at MIT's computer science and artificial intelligence laboratory studied not only whether AI was able to perform a task, but also whether it made economic sense for firms to replace humans performing those tasks in the wider context of the labor market. They found that while computer vision AI is today capable of automating tasks that account for 1.6% of worker wages in the U.S. economy (excluding agriculture), only 23% of those wages (0.4% of the economy as a whole) would, at today's costs, be cheaper for firms to automate instead of paying human workers.

Adversarial examples resulting from instability of current computer vision models are an extremely important topic due to their potential to compromise any application. In this paper we demonstrate that instability is inevitable due to a) symmetries (translational invariance) of the data, b) the categorical nature of the classification task, and c) the fundamental discrepancy of classifying images as objects themselves. The issue is further exacerbated by non-exhaustive labelling of the training data. Therefore we conclude that instability is a necessary result of how the problem of computer vision is currently formulated. While the problem cannot be eliminated, through the analysis of the causes, we have arrived at ways how it can be partially alleviated. These include i) increasing the resolution of images, ii) providing contextual information for the image, iii) exhaustive labelling of training data, and iv) preventing attackers from frequent access to the computer vision system.

For human children as well as machine learning systems, a key challenge in learning a word is linking the word to the visual phenomena it describes. We explore this aspect of word learning by using the performance of computer vision systems as a proxy for the difficulty of learning a word from visual cues. We show that the age at which children acquire different categories of words is correlated with the performance of visual classification and captioning systems, over and above the expected effects of word frequency. The performance of the computer vision systems is correlated with human judgments of the concreteness of words, which are in turn a predictor of children's word learning, suggesting that these models are capturing the relationship between words and visual phenomena.

Image Acquisition: The first step in computer vision is to acquire an image or video feed. This can be done using a camera or other imaging device. Pre-Processing: Once the image is acquired, it needs to be pre-processed to make it easier for the computer to analyze. This may involve noise reduction, image enhancement, or color correction. Feature Extraction: In this step, the computer analyzes the image to identify and extract specific features relevant to the task.

Computer vision is critical in the applications of facial recognition. It enables computers to match the identities of their faces. The algorithms detect facial features with face profiles in databases.