Notes on image annotation

By Adela Barriuso, Antonio Torralba. 2012

35 citations recorded by the source corpus.

We are under the illusion that seeing is effortless, but frequently the visual system is lazy and makes us believe that we understand something when in fact we don't. Labeling a picture forces us to become aware of the difficulties underlying scene understanding. Suddenly, the act of seeing is not effortless anymore. We have to make an effort in order to understand parts of the picture that we neglected at first glance. In this report, an expert image annotator relates her experience on segmenting and labeling tens of thousands of images. During this process, the notes she took try to highlight the difficulties encountered, the solutions adopted, and the decisions made in order to get a consistent set of annotations. Those annotations constitute the SUN database.

Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes (2021, methodology): “Yet due to their data-hungry nature, these methods rely on large volumes of annotated data for training, which can be challenging and expensive, especially in specialized application scenarios [5].”
Generative Negative Text Replay for Continual Vision-Language Pretraining (2022, methodology): “Concretely, we evaluate models on eight representative datasets including CIFAR10 [26], CIFAR-100 [26], Caltech101 [17], Oxford 102 Flower(Flowers102) [32], Food101 [6], SUN397 [5], Describable Textures Dataset (DTD) [12], and ImageNet [14].”
SUN Database: Exploring a Large Collection of Scene Categories (2014, methodology): “We describe the details for object labeling protocol in a separate technical report [5].”
One-shot Texture Segmentation (2018, background): “Virtually all existing segmentation tasks [9, 22, 46] are based on manual human labels which are usually hard to get [46, 47, 2, 5] and concentrate on high-level semantic concepts.”