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Unsupervised Visual Representation Learning by Context Prediction

This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation. Given only a large, unlabeled image collection, we extract random pairs of patches from each image and train a convolutional neural net to predict the position of the second patch relative to the first. We argue that doing well on this task requires the model to learn to recognize objects and their parts. We demonstrate that the feature representation learned using this within-image context indeed captures visual similarity across images. For example, this representation allows us to perform unsupervised visual discovery of objects like cats, people, and even birds from the Pascal VOC 2011 detection dataset. Furthermore, we show that the learned ConvNet can be used in the RCNN framework and provides a significant boost over a randomly-initialized ConvNet, resulting in state-of-the-art performance among algorithms which use only Pascal-provided training set annotations.

Citation

@inproceedings{doersch2015unsupervised,
  title={Unsupervised visual representation learning by context prediction},
  author={Doersch, Carl and Gupta, Abhinav and Efros, Alexei A},
  booktitle={ICCV},
  year={2015}
}

Models and Benchmarks

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In this page, we provide benchmarks as much as possible to evaluate our pre-trained models. If not mentioned, all models were trained on ImageNet1k dataset.

VOC SVM / Low-shot SVM

The Best Layer indicates that the best results are obtained from which layers feature map. For example, if the Best Layer is feature3, its best result is obtained from the second stage of ResNet (1 for stem layer, 2-5 for 4 stage layers).

Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.

Model	Config	Best Layer	SVM	k=1	k=2	k=4	k=8	k=16	k=32	k=64	k=96
model	resnet50_8xb64-steplr-70e

ImageNet Linear Evaluation

The Feature1 - Feature5 don’t have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to resnet50_mhead_8xb32-steplr-90e.py for details of config.

The AvgPool result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to file name for details of config.

Model	Config	Feature1	Feature2	Feature3	Feature4	Feature5	AvgPool
model	resnet50_8xb64-steplr-70e

iNaturalist2018 Linear Evaluation

Please refer to resnet50_mhead_8xb32-steplr-84e_inat18.py and file name for details of config.

Model	Config	Feature1	Feature2	Feature3	Feature4	Feature5	AvgPool
model	resnet50_8xb64-steplr-70e

Places205 Linear Evaluation

Please refer to resnet50_mhead_8xb32-steplr-28e_places205.py and file name for details of config.

Model	Config	Feature1	Feature2	Feature3	Feature4	Feature5	AvgPool
model	resnet50_8xb64-steplr-70e

Semi-Supervised Classification

• In this benchmark, the necks or heads are removed and only the backbone CNN is evaluated by appending a linear classification head. All parameters are fine-tuned.

• When training with 1% ImageNet, we find hyper-parameters especially the learning rate greatly influence the performance. Hence, we prepare a list of settings with the base learning rate from {0.001, 0.01, 0.1} and the learning rate multiplier for the head from {1, 10, 100}. We choose the best performing setting for each method. The setting of parameters are indicated in the file name. The learning rate is indicated like 1e-1, 1e-2, 1e-3 and the learning rate multiplier is indicated like head1, head10, head100.

• Please use –deterministic in this benchmark.

Please refer to the directories configs/benchmarks/classification/imagenet/imagenet_1percent/ of 1% data and configs/benchmarks/classification/imagenet/imagenet_10percent/ 10% data for details.

Model	Pretrain Config	Fine-tuned Config	Top-1 (%)	Top-5 (%)
model	resnet50_8xb64-steplr-70e

Detection

The detection benchmarks includes 2 downstream task datasets, Pascal VOC 2007 + 2012 and COCO2017. This benchmark follows the evluation protocols set up by MoCo.

Pascal VOC 2007 + 2012

Please refer to faster_rcnn_r50_c4_mstrain_24k.py for details of config.

Model	Config	mAP	AP50
model	resnet50_8xb64-steplr-70e

COCO2017

Please refer to mask_rcnn_r50_fpn_mstrain_1x.py for details of config.

Model	Config	mAP(Box)	AP50(Box)	AP75(Box)	mAP(Mask)	AP50(Mask)	AP75(Mask)
model	resnet50_8xb64-steplr-70e

Segmentation

The segmentation benchmarks includes 2 downstream task datasets, Cityscapes and Pascal VOC 2012 + Aug. It follows the evluation protocols set up by MMSegmentation.

Pascal VOC 2012 + Aug

Please refer to file for details of config.

Model	Config	mIOU
model	resnet50_8xb64-steplr-70e

Cityscapes

Please refer to file for details of config.

Model	Config	mIOU
model	resnet50_8xb64-steplr-70e