Learning Object Categories From Internet Image Searches

By Rob Fergus, Li Fei-Fei, Pietro Perona, Andrew Zisserman. 2010

100 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Learning Video Representations from Textual Web Supervision (2020, methodology): “The most commonly-used approach is to use image search results, and to label each image with the query that was used to ﬁnd it [65, 15, 56, 6, 11, 13, 21, 10, 18, 35].”
Webly Supervised Learning of Convolutional Networks (2015, background): “We believe a more juicier direction is to feed more data into these networks (in fact, deeper networks would often need more data to train).”
The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition (2015, methodology): “4 is inspired by methods that propose to learn from web data [13, 8, 9, 60, 40, 29, 16] or reason about label noise [34, 57, 52, 47, 38].”
Large-Scale Weakly-Supervised Pre-Training for Video Action Recognition (2019, background): “Dataset sources: Many prior approaches use Internet as a natural source of content [6, 12, 13, 18, 24, 25, 34, 37, 44, 46, 51, 58, 67] and the associated search queries, hashtags, or user-comments as labels to construct datasets.”
Learning from Noisy Labels with Distillation (2017, background): “Learning with noisy labels is an important technique with many applications [8] [4].”
A Big Data Analytics Method for Tourist Behaviour Analysis (2017, unknown): “A small number of sample images and visual words have been shown to be sufficient for visual word construction [17], whereas higher numbers of visual words do not”
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise (2017, background): “In practice, for fast development of new image recognition tasks, a widely used surrogate is to automatically collect noisy labeled data from Internet [6, 11, 25].”
Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach (2016, background): “…labellers and — especially for images — the use of search engines to query instances by a key-word, assuming the keyword as a valid label [5, 35, 3, 29, 17] Both approaches offer the possibility to scale the acquisition of training labels, but invariably result in the introduction of…”
Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey (2019, background): “[181, 182], design networks that can learn from unlimited web data with no human supervision [37, 38, 39, 40].”
Probabilistic End-To-End Noise Correction for Learning With Noisy Labels (2019, background): “On the other hand, we can easily collect a large scale dataset with noisy annotations through image search engines [4, 11, 20].”
Learning Everything about Anything: Webly-Supervised Visual Concept Learning (2014, background): “Learning from web images: Due to the complexity of the detection task and the higher supervision requirements, most previous works [4, 19, 28, 38, 44, 48] on using web images have focused on learning models only for image classification.”