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Found 9 papers, 5 papers with code
Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information
We showcase its application to the landmark recognition domain, presenting a detailed analysis and the final fairer landmark rankings.
Towards A Fairer Landmark Recognition Dataset
To create a more comprehensive and equitable dataset, we start by defining the fair relevance of a landmark to the world population.
Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food
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 health.
Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
GLDv2 is the largest such dataset to date by a large margin, including over 5M images and 200k distinct instance labels.
Ranked #1 on
Landmark Recognition
on Google Landmarks Dataset v2 (recognition, validation)
(using extra training data)
Unifying Deep Local and Global Features for Image Search
Image retrieval is the problem of searching an image database for items that are similar to a query image.
Detect-to-Retrieve: Efficient Regional Aggregation for Image Search
Then, we demonstrate how a trained landmark detector, using our new dataset, can be leveraged to index image regions and improve retrieval accuracy while being much more efficient than existing regional methods.
CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps
Image geolocalization is the task of identifying the location depicted in a photo based only on its visual information.
Ranked #1 on
Photo geolocation estimation
on Im2GPS
(Reference images metric)
BranchOut: Regularization for Online Ensemble Tracking With Convolutional Neural Networks
We propose an extremely simple but effective regularization technique of convolutional neural networks (CNNs), referred to as BranchOut, for online ensemble tracking.
Large-Scale Image Retrieval with Attentive Deep Local Features
We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELF (DEep Local Feature).
Ranked #2 on
Image Retrieval
on Oxf105k
