Scalable multi-label annotation

By Jia Deng, Olga Russakovsky, Jonathan Krause, Michael S. Bernstein, Alexander C. Berg, Li Fei-Fei. 2014

176 citations recorded by the source corpus.

Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

Microsoft COCO: Common Objects in Context (2014, methodology): “Next, each image was labeled as containing particular object categories using a hierarchical labeling approach [18].”
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation (2013, methodology): “lidate that they do not contain any instances of their associated class. The negative image sets were not used in this work. More information on how ILSVRC was collected and annotated can be found in [11,36]. The nature of these splits presents a number of choices for training R-CNN. The train image”
VirTex: Learning Visual Representations from Textual Annotations (2020, neutral): “To collect classification labels, typically human experts first build an ontology of categories [3, 4, 30, 31], then complex crowdsourcing pipelines are used to elicit labels from non-expert users [32, 33].”
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop (2015, methodology): “Clever methods have been proposed to improve the efﬁciency of human-in-the-loop annotation (e.g., [2, 5, 13, 20, 21]).”
A Corpus for Reasoning about Natural Language Grounded in Photographs (2018, methodology): “Hypernyms mosquito net → mosquito net protection Add direct or indirect hypernyms from Word-Net (Miller, 1993; Deng et al., 2014).”
Variational Denoising Network: Toward Blind Noise Modeling and Removal (2019, methodology): “[45], we collected a set of source images to train the network, including 432 images from BSD [5], 400 images from the validation set of ImageNet [12] and 4744 images from the Waterloo Exploration Database [26].”
Efficient Object Annotation via Speaking and Pointing (2019, methodology): “In Table 1, we also compare to the speed of Deng et al. (2014), the method that was used to annotate this dataset.”
Mining Object Parts from CNNs via Active Question-Answering (2017, background): “Inspired by active learning [38, 41, 22], the idea of learning from questionanswering has been used to learn object models [9, 27, 39].”
Crowdsourcing in Computer Vision (2016, unknown): “Donahue and Grauman [2011] collect “annotator rationales” which are explanations about why a certain label is present in an image.”
Best of both worlds: Human-machine collaboration for object annotation (2015, methodology): “The accuracy rates and costs (in median human time [13]) are reported in Table 2.”
COCO Attributes: Attributes for People, Animals, and Objects (2016, methodology): “We identify additional cost-saving annotation strategies by imitating successes in multi-class recognition [25,26].”
Embracing Error to Enable Rapid Crowdsourcing (2016, methodology): “One such method [14] annotated ImageNet (a popular large dataset for image classification) effectively with a useful insight: they realized that its classes could be grouped together into higher semantic concepts.”
Objects365: A Large-Scale, High-Quality Dataset for Object Detection (2019, methodology): “Motivated by existing datasets like ImageNet [5] and COCO [24] as well as the discussion of scalable multi-class annotation in [6], we de-sign our annotation pipeline as the following three steps.”
Region-Based Convolutional Networks for Accurate Object Detection and Segmentation (2016, background): “collected and annotated can be found in [65], [66].”