A large-scale hierarchical image database
By Jia Deng. 2009
1,091 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Xiaogang Wang
- Dacheng Tao
- Ming-Hsuan Yang
- Trevor Darrell
- Ling Shao
- Andrea Vedaldi
- Andrew Zisserman
In Evaluation & Benchmarks
- Li Fei-Fei
- Xiaogang Wang
- Olga Russakovsky
- Trevor Darrell
- Haibin Ling
- Andrea Vedaldi
- Hongsheng Li
- Alan L. Yuille
Citations in the corpus
- Advanced Steel Microstructural Classification by Deep Learning Methods (2017, background): “,[22] with 7 layers was the winner of ImageNet Large Scale Visual Recognition Challenge (ILSVRC)[23]in 2012 which is one of the most well-known object classification task challenges in computer vision community.”
- Learned Image Compression with Mixed Transformer-CNN Architectures (2023, methodology): “For training, we randomly choose 300k images of size larger than 256 × 256 from ImageNet [8], and randomly crop them with the size of 256 × 256 during the training process.”
- Asymmetric multi-task learning based on task relatedness and loss (2016, methodology): “For example, in ImageNet dataset (Deng et al., 2009) for object categorization task, some object categories have more than 3000 images per class, while some others have less than 10 instances.”
- PSNet: prostate segmentation on MRI based on a convolutional neural network (2018, neutral): “The initial work was proposed for image classification(6) using a big data set of natural images called ImageNet.(7) Currently, most deep learning models are for imagelevel classification.”
- Rico: A Mobile App Dataset for Building Data-Driven Design Applications (2017, background): “Unlike static research datasets such as ImageNet [12], Rico will become outdated over time if new apps are not continually crawled and their entries updated in the database.”
- Single Shot Temporal Action Detection (2017, methodology): “For spatial and temporal network, we follow the same training strategy described in [40] which uses the VGGNet-16 pre-trained on ImageNet [4] to intialize the network and fine-tunes it on training set.”