49 citations · 135 across the 8 of their papers we have counts for
8 papers
Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information
Zu Kim, André Araujo, Bingyi Cao +5
There has been increasing awareness of ethical issues in machine learning, and fairness has become an important research topic. Most fairness efforts in computer vision have been f…
Towards A Fairer Landmark Recognition Dataset
Zu Kim, André Araujo, Bingyi Cao +5
We introduce a new landmark recognition dataset, which is created with a focus on fair worldwide representation. While previous work proposes to collect as many images as possible…
Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food
Quin Thames, Arjun Karpur, Wade Norris +4
Understanding the nutritional content of food from visual data is a challenging computer vision problem, with the potential to have a positive and widespread impact on public healt…
Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
Tobias Weyand, Andre Araujo, Bingyi Cao +1
While image retrieval and instance recognition techniques are progressing rapidly, there is a need for challenging datasets to accurately measure their performance -- while posing…
Unifying Deep Local and Global Features for Image Search
Bingyi Cao, Andre Araujo, Jack Sim
Image retrieval is the problem of searching an image database for items that are similar to a query image. To address this task, two main types of image representations have been s…
Detect-to-Retrieve: Efficient Regional Aggregation for Image Search
Marvin Teichmann, Andre Araujo, Menglong Zhu +1
Retrieving object instances among cluttered scenes efficiently requires compact yet comprehensive regional image representations. Intuitively, object semantics can help build the i…