Review: BoWNet, Bags of Visual Words Predictions

Teacher-Student-Based Self-Supervised Learning Using Bag of Visual Words, Outperforms MoCo, PIRL, & Jigsaw

  • A self-supervised approach is proposed based on spatially dense image descriptions that encode discrete visual concepts, called visual words.
  • The feature maps of a first pretrained self-supervised convnet is quantized, over a k-means based vocabulary.
  • Then, another convnet is trained to predict the histogram of visual words of an image (i.e., its Bag-of-Words representation) given as input a perturbed version of that image.
  • Thus, BoWNet forces the convnet to learn perturbation-invariant and context-aware image features.

Outline

  1. BoWNet
  2. Experimental Results

1. BoWNet

BoWNet Framework
  • The goal is to learn in an unsupervised way a feature extractor or convnet model Φ(·) parameterized by θ that, given an image x, produces a “good” image representation Φ(x) for other downstream tasks.

1.1. Building Spatially Dense Discrete Descriptions q(x)

Self-supervised ImageNet-pretrained RotNet ˆΦ(·)
  • Given a training image x, the first step for our method is to create a spatially dense visual words-based description q(x) using the pre-trained convnet ˆΦ(·).
  • Self-supervised ImageNet-pretrained RotNet is used as ˆΦ(·).
  • Specifically, let ˆΦ(x) be a feature map (with ˆc channels and ˆh׈w spatial size) produced by ˆΦ(·) for input x, and ˆu(x) the ˆc-dimensional feature vector at the location u ∈ {1, · · · ,U}, where Uh·ˆw.
Spatially Dense Discrete Descriptions q(x)
  • To generate the description q(x) = [q1(x), …, qU(x)], ˆΦ(x) is densely quantized using a predefined vocabulary V = [v1, …, vK] of ˆc-dimensional visual word embeddings, where K is the vocabulary size.
  • where the vocabulary V is learned by applying the k-means algorithm with K clusters to a set of feature maps extracted from the dataset X, i.e., by optimizing the following objective:
  • where the visual word embedding vk is the centroid of the k-th cluster.

1.2. Generating Bag-of-Words Representations y(x)

Histogram or Binary Encoding y(x)
  • There are two ways: Histogram version and Binary version.
  • Histogram version: A K-dimensional vector whose k-th element yk(x) either encodes the number of times the k-th visual word appears in image x:
  • Binary version: or indicates if the k-th visual word appears in image x:
  • where 1[·] is the indicator operator.
  • The binary version is used for ImageNet and the histogram version for CIFAR-100 and MiniImageNet.
  • To convert y(x) into a probability distribution over visual words, y(x) is L1-normalized.

1.3. Learning to “Reconstruct” BoW Ω(Φ(~x))

BoW Prediction/Reconstruction Ω(Φ(~x))
  • Specifically, for g(.), it consists of color jittering, random grayscale, random crop, scale distortion, and horizontal flipping.
  • In addition, CutMix augmentation is also used.
  • The feature representation produced by model Φ(·) is c-dimensional.
  • Ω(.) takes this feature as input and outputs a K-dimensional softmax distribution over the K visual words of the BoW representation. This prediction layer is implemented with a linear-plus-softmax layer:
  • W=[w1, …, wK] are the K c-dimensional weight vectors (one per visual word) of the linear layer. But instead using W directly, a L2-normalized version of W is used:
  • with a unique learnable magnitude γ for all the weight vectors.
  • The reason for this reparametrization of the linear layer is because the distribution of visual words in the dataset tends to be unbalanced and, so, without such a reparametrization the network would attempt to make the magnitude of each weight vector proportional to the frequency of its corresponding visual word.

1.4. Self-Supervised Training Objective

  • The training loss that is to minimize for learning the convnet model Φ(·) is the expected cross-entropy loss between the predicted softmax distribution Ω(Φ(˜x)) and the BoW distribution y(x):
  • The self-supervised method can be applied iteratively, using each time the previously trained model ˆΦ(·) for creating the BoW representation.
  • The model learned from the first iteration already achieves very strong results. As a result, only a few more iterations (e.g., one or two) might be applied after that.

2. Experimental Results

2.1. CIFAR-100 & MiniimageNet

CIFAR-100 linear classifier and few-shot results with WRN-28–10.
MiniImageNet linear classifier and few-shot results with WRN-28–4.
  • K=2048 visual words is used.
  • Applying BoWNet iteratively (entries BoWNet ×2 and BoWNet ×3) further improves the results (except the 1-shot accuracy).
  • Also, BoWNet outperforms by a large margin the CIFAR-100 linear classification accuracy of the recent AMDIM [5].
  • Comparing with Deeper Clustering, it got several absolute percentage points lower linear classification accuracy, which illustrates the advantage of using BoW as targets for self-supervision instead of the single cluster id of an image.
MiniImageNet linear classifier and few-shot results with WRN-28–4. Impact of base convnet.
  • It is noted that with a random base convnet, the performance of BoWNet drops. However, BoWNet still is significantly better than RotNet and RelLoc.

2.2. PASCAL VOC 2007 Classification

VOC07 image classification results for ResNet-50 Linear SVMs.
  • K=20000 visual words is used.
  • Interestingly, conv4-based BoW leads to better classification results for the conv5 layer of BoWNet, and conv5-based BoW leads to better classification results for the conv4 layer of BoWNet.

2.3. ImageNet and Place 205 Classification

ResNet-50 top-1 center-crop linear classification accuracy on ImageNet and Places205.
  • Furthermore, the accuracy gap on Places205 between our ImageNet-trained BoWNet representations and the ImageNet-trained supervised representations is only 0.9 points in pool5. This demonstrates that the self-supervised representations have almost the same generalization ability to the “unseen” (during training) Places205 classes as the supervised ones.
  • Concurrent MoCo [25] and PIRL [42] methods are also compared.

2.4. PASCAL VOC 2007 Object Detection

Object detection with Faster R-CNN fine-tuned on VOC trainval07+12.
  • Faster R-CNN with a ResNet-50 backbone is used. The pre-trained BoWNet is fine-tuned on trainval07+12 and evaluated on test07.
  • BoWNet outperforms the supervised ImageNet pretrained model, which is fine-tuned in the same conditions as BoWNet. So, the self-supervised representations generalize better to the VOC detection task than the supervised ones.

The BoWNet framework exhibits a Teacher-Student architecture where contrastive learning is not required.

Reference

[2020 CVPR] [BoWNet]
Learning Representations by Predicting Bags of Visual Words

Unsupervised/Self-Supervised Learning

1993–2017 … 2018 [RotNet/Image Rotations] [DeepCluster] [CPC/CPCv1] [Instance Discrimination] 2019 [Ye CVPR’19] 2020 [CMC] [MoCo] [CPCv2] [PIRL] [SimCLR] [MoCo v2] [iGPT] [BoWNet]

My Other Previous Paper Readings

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