Data Augmentation from Sketch

State of the art machine learning methods need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. Simulated data could serve for data augmentation provided that its appearance was comparable to the actual appearance of intra-operative acquisitions. Generative Adversarial Networks (GANs) are a powerful tool for artistic style transfer, but lack a criteria for selecting epochs ensuring also preservation of intra-operative content. We propose a multi-objective optimization strategy for a selection of cycleGAN epochs ensuring a mapping between virtual images and the intra-operative domain preserving anatomical content. Our approach has been applied to simulate intra-operative bronchoscopic videos and chest CT scans from virtual sketches generated using simple graphical primitives

An improved Siamese network for face sketch recognition

Face sketch recognition identifies the face photo from a large face sketch dataset. Some traditional methods are typically used to reduce the modality gap between face photos and sketches and gain excellent recognition rate based on a pseudo image which is synthesized using the corresponded face photo. However, these methods cannot obtain better high recognition rate for all face sketch datasets, because the use of extracted features cannot lead to the elimination of the effect of different modalities' images. The feature representation of the deep convolutional neural networks as a feasible approach for identification involves wider applications than other methods. It is adapted to extract the features which eliminate the difference between face photos and sketches. The recognition rate is high for neural networks constructed by learning optimal local features, even if the input image shows geometric distortions. However, the case of overfitting leads to the unsatisfactory performance of deep learning methods on face sketch recognition tasks. Also, the sketch images are too simple to be used for extracting effective features. This paper aims to increase the matching rate using the Siamese convolution network architecture. The framework is used to extract useful features from each image pair to reduce the modality gap. Moreover, data augmentation is used to avoid overfitting. We explore the performance of three loss functions and compare the similarity between each image pair. The experimental results show that our framework is adequate for a composite sketch dataset. In addition, it reduces the influence of overfitting by using data augmentation and modifying the network structure

A Recipe for Efficient SBIR Models: Combining Relative Triplet Loss with Batch Normalization and Knowledge Distillation

Sketch-Based Image Retrieval (SBIR) is a crucial task in multimedia retrieval, where the goal is to retrieve a set of images that match a given sketch query. Researchers have already proposed several well-performing solutions for this task, but most focus on enhancing embedding through different approaches such as triplet loss, quadruplet loss, adding data augmentation, and using edge extraction. In this work, we tackle the problem from various angles. We start by examining the training data quality and show some of its limitations. Then, we introduce a Relative Triplet Loss (RTL), an adapted triplet loss to overcome those limitations through loss weighting based on anchors similarity. Through a series of experiments, we demonstrate that replacing a triplet loss with RTL outperforms previous state-of-the-art without the need for any data augmentation. In addition, we demonstrate why batch normalization is more suited for SBIR embeddings than l2-normalization and show that it improves significantly the performance of our models. We further investigate the capacity of models required for the photo and sketch domains and demonstrate that the photo encoder requires a higher capacity than the sketch encoder, which validates the hypothesis formulated in [34]. Then, we propose a straightforward approach to train small models, such as ShuffleNetv2 [22] efficiently with a marginal loss of accuracy through knowledge distillation. The same approach used with larger models enabled us to outperform previous state-of-the-art results and achieve a recall of 62.38% at k = 1 on The Sketchy Database [30]

Paper-based Mixed Reality Sketch Augmentation as a Conceptual Design Support Tool

This undergraduate student paper explores usage of mixed reality techniques as support tools for conceptual design. A proof-of-concept was developed to illustrate this principle. Using this as an example, a small group of designers was interviewed to determine their views on the use of this technology. These interviews are the main contribution of this paper. Several interesting applications were determined, suggesting possible usage in a wide range of domains. Paper-based sketching, mixed reality and sketch augmentation techniques complement each other, and the combination results in a highly intuitive interface

3D Car Shape Reconstruction from a Single Sketch Image

Efficient car shape design is a challenging problem in both the automotive industry and the computer animation/games industry. In this paper, we present a system to reconstruct the 3D car shape from a single 2D sketch image. To learn the correlation between 2D sketches and 3D cars, we propose a Variational Autoencoder deep neural network that takes a 2D sketch and generates a set of multiview depth & mask images, which are more effective representation comparing to 3D mesh, and can be combined to form the 3D car shape. To ensure the volume and diversity of the training data, we propose a feature-preserving car mesh augmentation pipeline for data augmentation. Since deep learning has limited capacity to reconstruct fine-detail features, we propose a lazy learning approach that constructs a small subspace based on a few relevant car samples in the database. Due to the small size of such a subspace, fine details can be represented effectively with a small number of parameters. With a low-cost optimization process, a high-quality car with detailed features is created. Experimental results show that the system performs consistently to create highly realistic cars of substantially different shape and topology, with a very low computational cost

SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis

Synthesizing realistic images from human drawn sketches is a challenging problem in computer graphics and vision. Existing approaches either need exact edge maps, or rely on retrieval of existing photographs. In this work, we propose a novel Generative Adversarial Network (GAN) approach that synthesizes plausible images from 50 categories including motorcycles, horses and couches. We demonstrate a data augmentation technique for sketches which is fully automatic, and we show that the augmented data is helpful to our task. We introduce a new network building block suitable for both the generator and discriminator which improves the information flow by injecting the input image at multiple scales. Compared to state-of-the-art image translation methods, our approach generates more realistic images and achieves significantly higher Inception Scores.Comment: Accepted to CVPR 201

Sketch-a-Net that Beats Humans

We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans. Our superior performance is a result of explicitly embedding the unique characteristics of sketches in our model: (i) a network architecture designed for sketch rather than natural photo statistics, (ii) a multi-channel generalisation that encodes sequential ordering in the sketching process, and (iii) a multi-scale network ensemble with joint Bayesian fusion that accounts for the different levels of abstraction exhibited in free-hand sketches. We show that state-of-the-art deep networks specifically engineered for photos of natural objects fail to perform well on sketch recognition, regardless whether they are trained using photo or sketch. Our network on the other hand not only delivers the best performance on the largest human sketch dataset to date, but also is small in size making efficient training possible using just CPUs.Comment: Accepted to BMVC 2015 (oral