Pose Augmentation: Class-agnostic Object Pose Transformation for Object Recognition
Object pose increases interclass object variance which makes object recognition from 2D images harder. To render a classifier robust to pose variations, most deep neural networks try to eliminate the influence of pose by using large datasets with many poses for each class. Here, we propose a different approach: a class-agnostic object pose transformation network (OPT-Net) can transform an image along 3D yaw and pitch axes to synthesize additional poses continuously. Synthesized images lead to better training of an object classifier. We design a novel eliminate-add structure to explicitly disentangle pose from object identity: first âeliminateâ pose information of the input image and then âaddâ target pose information (regularized as continuous variables) to synthesize any target pose. We trained OPT-Net on images of toy vehicles shot on a turntable from the iLab-20M dataset. After training on unbalanced discrete poses (5 classes with 6 poses per object instance, plus 5 classes with only 2 poses), we show that OPT-Net can synthesize balanced continuous new poses along yaw and pitch axes with high quality. Training a ResNet-18 classifier with original plus synthesized poses improves mAP accuracy by 9% over training on original poses only. Further, the pre-trained OPT-Net can generalize to new object classes, which we demonstrate on both iLab-20M and RGB-D. We also show that the learned features can generalize to ImageNet.
Keywords:pose transform, data augmentation, object recognition, GANs
1 Introduction and related work
In object recognition from 2D images, object pose has a significant influence on performance. An image depends on geometry (shape), photometry (illumination and material properties of objects) and dynamics (as objects move) of the scene. Thus, every image is a mixture of instance-specific information and nuisance factors , such as 3D viewpoint, illumination, occlusions, shadows, etc. Nuisance factors often depend on the task itself. Specifically, in object recognition from 2D images, we care for instance-specific information like shape, while the dynamics of pose is a nuisance that often degrades classification accuracy .
Deep convolution neural networks (CNNs) have achieved great success in object recognition [22, 34, 14, 37, 16] and many other tasks, such as object detection [10, 31, 30, 9], image segmentation [32, 27, 13], etc. Most research tries to discount pose, by eliminating pose information or improving pose robustness of a classifier. Typical CNN architectures, such as LeNet  AlexNet  and VGG  use convolution layers and pooling layers to make the high-level feature representations invariant to object pose over some limited range . In contrast, recent results have shown that explicitly modeling pose information can help an object recognition task [42, 2, 39, 1]. Some approaches use multi-task learning where pose information can be an auxiliary task or regularization to improve the main object recognition task [41, 40, 17, 36]. These neural networks have the potential to disentangle content from their instantiation attributes [29, 43, 12]. Training on multiple views of the object can improve recognition accuracy . A common method is collecting all poses of the object and creating a pose-balanced dataset, with the hope that pose variations will average out. However, collecting pose-balanced datasets is hard and expensive. One notable such dataset is iLab-20M which comprises 22 million images of 704 toy vehicles captured by 11 cameras while rotating on a turntable . Here, we use a subset of this data to learn about pose transformations, then transferring this knowledge to new datasets (RGB-D , ImageNet ).
2D images can be seen as samples of 3D poses along yaw and pitch axes (Fig.2(a)). We want our OPT-Net to imitate the 3D pose transformation along these two axes. Thus given any single pose image, we can ’rotate’ the object along yaw and pitch axes to any target pose. Instead of directly training a transformation model to continuously ’rotate’ images, we start with a discrete transform, which is easier to constrain. Then we can make the pose representation continuous and normalize the continuous transform process. Here, we use sampled discrete poses along yaw and pitch as our predefined poses (Fig.2(b), 6 poses along the yaw axis and 3 poses along pitch axis). We treat different object poses as different domains so that discrete pose transformation can be seen as an image-to-image translation task, where a generative model can be used to synthesize any target pose given any input pose. Recently, Generative Adversarial Networks (GAN)  have shown a significant advantage in transforming images from one modality into another modality [28, 18, 33, 44, 21]. GANs show great performance in various tasks, such as style transfer [5, 20], domain adaptation [15, 38], etc. However, there is a high cost in our task, because we should train specific GANs for all pairs of poses . StarGAN  and CollaGAN  proposed a method for multi-domain mapping with one generator and showed great results in appearance changes such as hair color, age, and emotion transform. However, pose transform creates a large, nonlinear spatial change between input and output images. The traditional structure of the generators (Unet , Vnet ) has few shared structures which satisfy all randomly paired pose transformation. It makes StarGAN training hard to converge (see Exp 4.1).
Learning a better representation could also reduce variance due to pose.  tried to learn better representation features to disentangle identity rotation and view features. InfoGAN  learns disentangled representations in an unsupervised manner.  seeks a view-invariant representation shared by views.
To combine the idea of better representation and multi-domain image transformation, we propose a class-agnostic object pose transformation neural network (OPT-Net), which first transforms the input image into a canonical space with pose-invariant representation and then transform it to the target domain. We design a novel eliminate-add structure of the OPT-Net and explicitly disentangle pose from object identity: OPT-Net first âeliminatesâ the pose information of the input image and then âaddsâ target pose information to synthesize any target pose. Convolutional regularization is first used to implicitly regularize the representation to keep only the key identification information that may be useful to any target pose. Then, our proposed pose-eliminate module can explicitly eliminate the pose information contained in the canonical representation by adversarial learning. We also add a discriminator leveraging pose classification and image quality classification to supervise the optimization of transforming.
Overall our contributions are multifold: (1) developed OPT-Net, a novel class-agnostic object pose transformation network with an eliminate-add structure generator that learns the class-agnostic transformation among object poses by turning the input into a pose-invariant canonical representation. (2) design a continuous representation of 3D object pose and achieve continuous pose transforming in 3D, which can be learned from limited discrete sampled poses and adversarial regularization. (3) demonstrated the generative OPT-Net significantly boosts the performance of discriminative object recognition models. (4) showed OPT-Net learns class-agnostic pose transformations, generalizes to out-of-class categories and transfers well to other datasets like RGB-D and ImageNet.
2 Object Pose Transforming Network
As shown in Fig. 3, the proposed OPT-Net has an eliminate-add structure generator, a discriminator and a pose-eliminate module.
2.1 Eliminate-add structure of the generator
The generator () of OPT-Net transforms an input object pose image x into a target object pose y conditioned on the target pose label , y. Different from the hair color, gender and age transform, which have more appearance transfer with smaller shape changes, object pose transformation creates large shape differences. Our eliminate-add structure generator (Fig.3 (a)) first turns the input pose image into a pose-invariant canonical representation by âeliminatingâ pose information, and then âaddsâ target pose information to turn the representation into the target pose. As shown in Fig. 3(b), given an input image, we randomly select the target pose domain. We do not input target pose along with the input image. Instead, in the âeliminateâ part, the first several convolution layers with stride are used to implicitly regularize the preserved representation features. This implicit regularization makes the representation features contain only key information for the transformation (appearance, color, shape), and eliminates useless information which may hinder transformation (pose). At the same time (Fig. 3(b)), the âpose-eliminate moduleâ () explicitly forces the representation to contain as little pose information as possible, by predicting equal probability for every pose. After both implicit and explicit elimination of pose information, the input image is turned to a pose-invariant canonical representation space. We then âaddâ the target pose information by concatenating it with the representation feature map. The remaining layers in the generative model transform the concatenated features into the target pose image. This eliminate-add structure is shared and can be used for any pose transformation. This shared structure makes the generator easy to converge. To control the translation direction, as shown in Fig. 3(b), we use an auxiliary classifier as discriminator to guide the image quality and pose transform. Given one image, the discriminator has two outputs, the probability that the input image is real, which represents the quality of the synthesized image, and the output pose, which should match the desired target pose,
2.2 Pose-eliminate module
The pose-eliminate module () takes the preserved representation feature as input and outputs pose classification . can be treated as a discriminator which forms an adversarial learning framework with the âeliminateâ part of the generator (). The canonical representation features of real images with pose labels are used to train . We use Cross-Entropy loss to make predict the correct pose from the pose-invariant feature after . Different from traditional adversarial training, when using to train , we want the generator to eliminate all pose information in the pose-invariant feature, which makes produce equal output probability for every pose. We use the uniform probability () as the ground truth label to compute the pose-eliminate loss, which is used to optimize the .
2.3 Continuous pose transforming training
We design a 2-dimension linear space to represent pitch and yaw values, in which we could interpolate and achieve continuous pose representation (Fig.1) . The yaw and pitch values can be duplicated as a matrix that has the same dimension as the canonical representation features, which is easy to be concatenated. The mask vector has N (we use 3) dimensions along the channel dimension which can be adjusted depending on the canonical representation features channel. We start the training on discrete sampled poses (which can be represented as integer in linear space). After the network has converged, we randomly sample decimal poses as target poses and use a style consistency loss to regularize the synthesized images, which keeps pose representation consistent along yaw and pitch axes.
2.4 Loss Function
Our goal is to train a generator that learns object pose transformations along yaw and pitch axes. The overall loss is formed by adversarial loss, domain classification loss, reconstruction loss, pose-eliminate loss and style consistency loss.
Adversarial Loss. The adversarial loss is used to make the synthesized image indistinguishable from real images.
represent the probability that input x belongs to the real images given by . The generator tries to minimize the loss, while the discriminator tries to maximize it.
Pose Classification Loss. The pose classification loss is used to guide the pose transformation which makes the synthesized image belong to the target pose . This pose classification loss is used to optimize both and . The pose classification loss of is defined as
The loss for is similar to a traditional Cross-Entropy loss for classification, where means the predicted probability of real image x belongs to the ground truth pose label . The pose classification loss of is defined as
tries to minimize this loss to make the synthesized fake image be classified as the target pose .
Reconstruction Loss. To make the synthesized image preserve the content information and change only the object pose, as shown in fig3(c), we use the cycle consistency loss  to optimize .
where can reconstruct the original image by transforming the synthesized fake target pose image back to the original pose . norm is used as reconstruction loss.
Pose-eliminate Loss. In the eliminate-add structure of , to eliminate the pose information in preserved canonical representation features, we designed pose-eliminate loss to optimize the pose eliminate module ( ) and the eliminate part of ,( ). The pose eliminate loss is
where means the predicted probability of the canonical representation features of a real image belongs to the ground truth pose label . The pose eliminate loss for is defined as
where is the number of pose classes we defined, represent the pose label, , represent the probability of the synthesized canonical representation belongs to the pose. In ideal situations, the can hardly predict the correct pose from canonical representation features and output equal probability for every pose, which means the pose information is eliminated in preserved canonical features. We use equal prediction of every pose to optimize instead of minimizing the pose classification accuracy of to avoid a âcheated optimizeâ that tries to predict all input to a fixed pose class.
Style consistency Loss. After the converge of the previous loss, we randomly sample decimal target pose instead of all integers to make continuous pose transforming, the style consistency loss can regularize the synthesized images. The equation of style consistency loss is same as adversarial loss above, but the target pose is randomly sampled decimal value along yaw and pitch axes.
Full Loss Function. Finally, we optimize:
where , and are hyper-parameters that control the relative importance of classification, reconstruction, and pose-eliminate losses.
3 Experimental Methods
iLab-20M dataset . The iLab-20M dataset is a controlled, parametric dataset collected by shooting images of toy vehicles placed on a turntable using 11 cameras at different viewing points. There are in total 15 object categories with each object having 25 160 instances. Each object instance was shot on more than 14 backgrounds (printed satellite images), in a relevant context (e.g., cars on roads, trains on rail tracks, boats on water). In total, 1,320 images were captured for each instance and background combinations: 11 azimuth angles (from the 11 cameras), 8 turntable rotation angles, 5 lighting conditions, and 3 focus values (-3, 0, and +3 from the default focus value of each camera). The complete dataset consists of 704 object instances, with 1,320 images per object-instance/background combination, almost 22M images (18 times of ImageNet).
RGB-D dataset. The RGB-D Object Dataset consists of 300 common household objects organized into 51 categories. This dataset was recorded using a Kinect style 3D camera. Each object was placed on a turntable and video sequences were captured for one whole rotation. For each object, there are 3 video sequences, each recorded with the camera mounted at a different height so that the object is shot from different viewpoints.
3.2 Network Implementation
Our OPT-Net consists of two parts, pose âeliminateâ part and pose âaddâ part . As shown in Fig. 3(b), the first has two convolution layers with stride size of two to downsample the input image. Then, three Resnet blocks  form the backbone of . The output is the pose-invariant canonical representation feature. The canonical representation feature is copied to different streams, one concatenates with the target pose mask, forming the input of to synthesize the target pose image. The other one is treated as the input of to predict the pose class. first has 6 blocks of Resnet as a backbone and ends with two up-sampling convolution layers to transform the canonical representation features to a target pose image, given a target pose information mask. For the discriminator , we adopt the PatchGAN  network.
has a traditional classification network structure, which has the first two convolution layers with stride size of 2 to downsample the input features, followed with 3 Resnet blocks as backbone and ending with one down sampling convolution layers. In the end, the feature map is resized to N-dimensional output vectors and we use Softmax to obtain the prediction of pose class.
We use Wasserstein GAN objective with a gradient penalty [1, 4] to stabilize the training process. We adjust the during training the generator, at the beginning epochs of training, improving the value of can accelerate the convergence of generator, which makes the synthesized fake pose image have meaningful corresponding spacial structure. We gradually reduce the value of . At the last ending part of the training, can be very small to make the optimization concentrate on improving the image quality.
4 Experiments and Results
We have five main experiments: in Section 4.1 on object pose transformation task, we compare OPT-Net with baseline StarGAN  and CycleGAN  by quantitatively and qualitatively comparing the synthesized object pose image quality. In Section 4.2, we use the OPT-Net as a generative model to help the training of a discriminative model for object recognition, by synthesizing missing poses and balancing a pose bias in the training dataset. In Section 4.3, we further show the class-agnostic transformation property of OPT-Net by generalizing the pretrained OPT-Net to new datasets. In Section 4.4, we study the influence of object pose information for objects which are mainly distinguishable by shape, as opposed to other features like color. Finally, in Section 4.5, we further demonstrate how the learned pose features in OPT-Net and object recognition model with the iLab-20M dataset can generalize to other datasets like ImageNet.
4.1 Object Pose Transformation Experiments
Because the baseline models can only do discrete pose transform, we fix the pitch value and use 6 different yaw viewpoints among the 88 different views of iLab-20M as our predefined pose to implement our OPT-Net. As is shown in Fig. 2, the selected 6 viewpoints have big spatial variance which can better represent the general object pose transformation task. In training set, each pose has nearly 26k images with 10 vehicle classes (Table 2). Each class contains 2080 different instances. The test set has the same 10 vehicle categories, but different instances than the training set. Both training and test datasets are 256x256 RGB images.
The training dataset is used to train our OPT-Net and the baseline models, StarGAN, and CycleGAN. Our OPT-Net has one generator, one discriminator and one pose-eliminate module; StarGAN has one generator and one discriminator, while CycleGAN needs 30 generators and 30 discriminators to achieve 6 pose transformations.
Qualitative evaluation. The experiment results are shown in Fig. 4. Compared with StarGAN, which struggles with large pose variations, the synthesized target pose images by OPT-Net are high quality with enough details, and the colors and appearances are similar to the real images. One possible reason is that our eliminate-add structure eliminates the conflicts between different directions on pose transformation and the pose-invariant representation breaks the barriers in making big spacial changes. Moreover, CycleGAN leads to inferior synthesized images in generalization on the test dataset. Thus our eliminate-add generator structure learns more generalized knowledge with less possible of over-fitting which can better generalized on new instances. Fig.1 shows more transformation results of OPT-Net.
Quantitative evaluation. The real target pose image of input is used as the ground truth label. To reduce background influence, we segment the foreground vehicle with the Graph-Based Image Segmentation method and only compute mean squared error (MSE) and peak signal to noise ratio (PSNR) of foreground between the synthesized target object pose and ground truth. (Table 1)
The result is the mean MSE and PSNR computed by 200 different instances, the MSE and PSNR for each instance is the average of 6 synthesized fake pose images. Table 1 demonstrates that the quality of synthesized target pose images by our OPT-Net is better than CycleGAN and StarGAN.
4.2 Object Recognition Experiment
We design an object recognition experiment to explore the performance of OPT-Net as a generative model to help the training of a discriminative model. Two different training datasets are tailored from iLab-20M, pose-unbalanced (P-UB) and pose-balanced (P-B). In P-UB (Table 2), 5 classes of vehicles (boat, car, semi, tank, and van) have all 6 pose images (same poses as 4.1), while the other 5 classes (bus, military car, monster, pickup, and train) have only two poses (pose2 and pose5), which has significant pose bias. In P-B, each category among 10 classes of vehicles has all 6 pose images (no pose bias). The test dataset is a pose-balanced dataset which contains different instances of the 10 classes of vehicles that were not in either training dataset (P-UB and P-B). The classification neural network we used is Resnet-18  (no pre-training).
We first train the classification model on P-UB and P-B, calculating the test accuracy of each class of vehicles on the test dataset. To evaluate the performance of OPT-Net, we first train it on P-UB to learn the object transformation ability. After training, for each category in P-UB which have only pose2 and pose5 (bus, military car, monster, pickup, and train), we use the trained OPT-Net to synthesize the missing 4 poses (pose1, pose3, pose4, pose6). We combine the synthesized images with P-UB and form a synthesized-pose-balanced (S-P-B) training dataset. To show continuous transforms, we also interpolate pose values and synthesize 5 new poses beyond the predefined ones, and form a synthesized-additional-pose-balanced (SA-P-B) training dataset. S-P-B and SA-P-B were used to train the same resnet-18 classification model from scratch and to calculate test accuracy of each class of vehicles in the test dataset. We also use common data augmentation methods (random crop, horizontal flip, scale resize, etc) to augment the P-UB dataset to the same number of images as P-B, called A-P-UB (Table 3).
The test accuracy of each class is shown in Table 4. From P-UB to S-P-B, the overall accuracy improved from 52.26% to 59.15%, which shows the synthesized missing pose images by OPT-Net can improve the performance of object recognition. It is also shown that OPT-Net, as a generative model, can help the discriminative model. Specifically, the vacant pose categories show significant improvement in accuracy: military improved by 11.68%, monster improved by 14.97%, pickup and train improved by 8.74% and 16.12% respectively. The comparison of S-P-B and A-P-UB shows that synthesized images by OPT-Net are better than traditional augmented images in helping object recognition. Because of the continuous pose transformation ability, our OPT-Net can synthesize additional poses different from the 6 poses in P-B. With these additional poses, SA-P-B (61.23%) performs even better than the P-U (59.20%), achieve 9% improvement compared with P-UB.
4.3 Class-agnostic Object Transformation Experiment
Our proposed OPT-Net can simultaneously make pose transformation on different classes of vehicles, which demonstrate that the learned object pose transformation has not fixed with object classes, it is a class-agnostic object pose transformation. To further explore the class-agnostic property of OPT-Net, we design experiments that generalize OPT-Netâs ability for object pose transformation from one dataset to other datasets.
15 categories of objects from RGB-D are used. They are both common household objects with big spatial variance between different object poses. Similar poses of objects in RGB-D are selected and defined as the same pose as iLab-20M. For each pose, RGB-D contains only about 100 images which cannot train our OPT-Net from scratch, thus we use RGB-D to finetune OPT-Net pre-trained on iLab-20M. We can see (Fig. 5) that our pre-trained OPT-Net can generalize well to other datasets, which demonstrates that OPT-Net is a class-agnostic object pose transformation framework.
To further explore the performance of OPT-Net as a generative model to help a discriminative model of object recognition, we split RGB-D into a pose-unbalanced (P-UB) training dataset, where each category randomly takes 3 poses among all 6 poses; pose-balanced (P-B), and test dataset similar to 4.2.
We first use P-UB to finetune the pretrained OPT-Net, and then use the trained OPT-Net to synthesize missing poses of household objects in RGB-D. The synthesized images and the original pose-unbalanced images form the synthesized pose balanced (S-P-B) training dataset. Similarly, to eliminate the influence of the number of training images, we created A-P-UB using common data augmentation methods. We trained Alexnet  on the 4 training datasets separately, and showed the test accuracy for each category in Table 5.
The (small) accuracy improvement in S-P-B compared with P-UB demonstrates that our pretrained OPT-Net can be generalized to different datasets after finetune, which can help the discriminative model in object recognition. While the overall improvement is small, below we show that this is not the case uniformly across all object categories.
4.4 Object Pose Significance on Different Object Recognition Tasks
Because the accuracy improvement in RGB-D is smaller than in iLab-20M, we tested whether this was the case across all object categories, or whether those which look more alike would benefit more from synthesized images from OPT-Net. Indeed, maybe classifying a black keyboard vs. a blue stapler can easily be achieved by size or color even without pose-dependent shape analysis. To verify our hypothesis, we use the confusion matrix of classification to select categories which are more confused by classifier: marker, comb, toothbrush, stapler, lightbulb, and sponge. We then assign different fixed poses to each category to improve overall pose variance and form P-UB-1 (randomly fix 1 pose for each category), P-UB-2 (randomly fix 2 poses for each category), and P-UB-3 (randomly fix 3 poses for each category) pose-unbalanced datasets (suppl. Table 1).
Similarly, we create 3 other training datasets using the same method as in 4.2 and 4.3: (S-P-B: use pretrained OPT-Net to synthesize the missing poses; P-B, and A-P-UB for each unbalanced datasets), and report the object recognition performance on the test dataset in Table 6.
The results in Table 6 demonstrate that object pose information has different degrees of impact on the object recognition task. Compared with the results in 4.3, where the improvement between P-UB and S-P-B is less than 1%, here, when the class variance is small, OPT-Net can improve more accuracy after synthesizing the missing poses in the unbalanced dataset. The accuracy improvement in experiment group 1 (P-UB-1 and S-P-B-1) is 8.1%. This result verified our hypothesis that pose balance is more important in small between-class variance object cognition tasks.
Meanwhile, comparing the different accuracy improvements in different experimental groups, group 2 (P-UB-2 and S-P-B-2) is 3.8%, while group 3 (P-UB-3 and S-P-B-3) is 0.1%. This demonstrates that when class-variance is fixed, the more pose bias we have, the more accuracy improvement we will get with the help of our OPT-Net pose transformation.
4.5 Generalization to Imagenet
We directly use the pretrained OPT-Net on iLab-20M to synthesize images of different poses on ImageNet (suppl. Fig. 1). Results are not as good and might be improved using domain adaptation in future work. However, the discriminator of OPT-Net makes decent prediction of image poses: Fig. 6 shows the top 8 ImageNet images for each of our 6 poses.
To test object recognition in ImageNet, we replace real images by OPT-Net synthesized images in S-P-B (4.2) and form a S-P-B (OPT-Net) dataset (all synthesized images). Similarly, we use StarGAN synthesized images form S-P-B (StarGAN). We use a resnet18 10-class vehicles classifier pretrained with this two synthesized datasets and predict 4 classes of vehicles in ImageNet which have similar meanings as iLab-20M, with good results on some classes like car (Table 7). To further explore generalization, we pretrian an AlexNet on S-P-B which synthesized pose images by StarGAN and OPT-Net respectively and then finetune it on ImageNet. Table 8 shows significantly better accuracy compared to training from scratch when using only a small number of images per class, demonstrating generalization from iLab-20M to ImageNet.
|Number of images per class||10||20||40|
|Alexnet (pretrain on S-P-B (StarGAN))||7.57||18.91||27.14|
|Alexnet (pretrain on S-P-B (OPT-Net))||10.94||19.75||27.82|
We proposed OPT-Net, a class-agnostic object pose transformation network (OPT-Net) to synthesize any target poses continuously given a single pose image. The proposed eliminate-add structure generator can first eliminate pose information and turn the input to a pose-invariant canonical representation, then adding the target pose information to synthesize the target pose image. OPT-Net also gives a more common framework to solve big variance continuous transformation problems. OPT-Net generated images have higher visual quality compared to existing methods. We also demonstrate that the OPT-Net, as a generative model can help the discriminative model in the object recognition task, which achieve a 9% accuracy improvement. We design experiments to demonstrate that pose balance is more important in small between-class variance object cognition tasks. Finally, we demonstrate the learned pose features in OPT-Net with the iLab-20M dataset can better generalize to other datasets like ImageNet.
This work was supported by the National Science Foundation (grant number CCF-1317433), C-BRIC (one of six centers in JUMP, a Semiconductor Research Corporation (SRC) program sponsored by DARPA), and the Intel and CISCO Corporations. The authors affirm that the views expressed herein are solely their own, and do not represent the views of the United States government or any agency thereof.
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