Unsupervised Domain Generalization by Learning a Bridge Across Domains

The ability to generalize learned representations across significantly different visual domains, such as between real photos, clipart, paintings, and sketches, is a fundamental capacity of the human visual system. In this paper, different from most cross-domain works that utilize some (or full) source domain supervision, we approach a relatively new and very practical Unsupervised Domain Generalization (UDG) setup of having no training supervision in neither source nor target domains. Our approach is based on self-supervised learning of a Bridge Across Domains (BrAD) - an auxiliary bridge domain accompanied by a set of semantics preserving visual (image-to-image) mappings to BrAD from each of the training domains. The BrAD and mappings to it are learned jointly (end-to-end) with a contrastive self-supervised representation model that semantically aligns each of the domains to its BrAD-projection, and hence implicitly drives all the domains (seen or unseen) to semantically align to each other. In this work, we show how using an edge-regularized BrAD our approach achieves significant gains across multiple benchmarks and a range of tasks, including UDG, Few-shot UDA, and unsupervised generalization across multi-domain datasets (including generalization to unseen domains and classes).

CHARTER: heatmap-based multi-type chart data extraction

The digital conversion of information stored in documents is a great source of knowledge. In contrast to the documents text, the conversion of the embedded documents graphics, such as charts and plots, has been much less explored. We present a method and a system for end-to-end conversion of document charts into machine readable tabular data format, which can be easily stored and analyzed in the digital domain. Our approach extracts and analyses charts along with their graphical elements and supporting structures such as legends, axes, titles, and captions. Our detection system is based on neural networks, trained solely on synthetic data, eliminating the limiting factor of data collection. As opposed to previous methods, which detect graphical elements using bounding-boxes, our networks feature auxiliary domain specific heatmaps prediction enabling the precise detection of pie charts, line and scatter plots which do not fit the rectangular bounding-box presumption. Qualitative and quantitative results show high robustness and precision, improving upon previous works on popular benchmarks

Detector-Free Weakly Supervised Grounding by Separation

Nowadays, there is an abundance of data involving images and surrounding free-form text weakly corresponding to those images. Weakly Supervised phrase-Grounding (WSG) deals with the task of using this data to learn to localize (or to ground) arbitrary text phrases in images without any additional annotations. However, most recent SotA methods for WSG assume the existence of a pre-trained object detector, relying on it to produce the ROIs for localization. In this work, we focus on the task of Detector-Free WSG (DF-WSG) to solve WSG without relying on a pre-trained detector. We directly learn everything from the images and associated free-form text pairs, thus potentially gaining an advantage on the categories unsupported by the detector. The key idea behind our proposed Grounding by Separation (GbS) method is synthesizing `text to image-regions' associations by random alpha-blending of arbitrary image pairs and using the corresponding texts of the pair as conditions to recover the alpha map from the blended image via a segmentation network. At test time, this allows using the query phrase as a condition for a non-blended query image, thus interpreting the test image as a composition of a region corresponding to the phrase and the complement region. Using this approach we demonstrate a significant accuracy improvement, of up to $8.5\%$ over previous DF-WSG SotA, for a range of benchmarks including Flickr30K, Visual Genome, and ReferIt, as well as a significant complementary improvement (above $7\%$) over the detector-based approaches for WSG.

DeepHist: Differentiable Joint and Color Histogram Layers for Image-to-Image Translation

We present the DeepHist - a novel Deep Learning framework for augmenting a network by histogram layers and demonstrate its strength by addressing image-to-image translation problems. Specifically, given an input image and a reference color distribution we aim to generate an output image with the structural appearance (content) of the input (source) yet with the colors of the reference. The key idea is a new technique for a differentiable construction of joint and color histograms of the output images. We further define a color distribution loss based on the Earth Mover's Distance between the output's and the reference's color histograms and a Mutual Information loss based on the joint histograms of the source and the output images. Promising results are shown for the tasks of color transfer, image colorization and edges $\rightarrow$ photo, where the color distribution of the output image is controlled. Comparison to Pix2Pix and CyclyGANs are shown.

Hue-Net: Intensity-based Image-to-Image Translation with Differentiable Histogram Loss Functions

We present the Hue-Net - a novel Deep Learning framework for Intensity-based Image-to-Image Translation. The key idea is a new technique termed network augmentation which allows a differentiable construction of intensity histograms from images. We further introduce differentiable representations of (1D) cyclic and joint (2D) histograms and use them for defining loss functions based on cyclic Earth Mover's Distance (EMD) and Mutual Information (MI). While the Hue-Net can be applied to several image-to-image translation tasks, we choose to demonstrate its strength on color transfer problems, where the aim is to paint a source image with the colors of a different target image. Note that the desired output image does not exist and therefore cannot be used for supervised pixel-to-pixel learning. This is accomplished by using the HSV color-space and defining an intensity-based loss that is built on the EMD between the cyclic hue histograms of the output and the target images. To enforce color-free similarity between the source and the output images, we define a semantic-based loss by a differentiable approximation of the MI of these images. The incorporation of histogram loss functions in addition to an adversarial loss enables the construction of semantically meaningful and realistic images. Promising results are presented for different datasets.

QANet - Quality Assurance Network for Microscopy Cell Segmentation

Tools and methods for automatic image segmentation are rapidly developing, each with its own strengths and weaknesses. While these methods are designed to be as general as possible, there are no guarantees for their performance on new data. The choice between methods is usually based on benchmark performance whereas the data in the benchmark can be significantly different than that of the user. We introduce a novel Deep Learning method which, given an image and a proposed corresponding segmentation, estimates the Intersection over Union measure (IoU) with respect to the unknown ground truth. We refer to this method as a Quality Assurance Network - QANet. The QANet is designed to give the user an estimate of the segmentation quality on the users own, private, data without the need for human inspection or labelling. It is based on the RibCage Network architecture, originally proposed as a discriminator in an adversarial network framework. Promising IoU prediction results are demonstrated based on the Cell Segmentation Benchmark.

Microscopy Cell Segmentation via Adversarial Neural Networks

We present a novel method for cell segmentation in microscopy images which is inspired by the Generative Adversarial Neural Network (GAN) approach. Our framework is built on a pair of two competitive artificial neural networks, with a unique architecture, termed Rib Cage, which are trained simultaneously and together define a min-max game resulting in an accurate segmentation of a given image. Our approach has two main strengths, similar to the GAN, the method does not require a formulation of a loss function for the optimization process. This allows training on a limited amount of annotated data in a weakly supervised manner. Promising segmentation results on real fluorescent microscopy data are presented. The code is freely available at: https://github.com/arbellea/DeepCellSeg.git

Microscopy Cell Segmentation via Convolutional LSTM Networks

Live cell microscopy sequences exhibit complex spatial structures and complicated temporal behaviour, making their analysis a challenging task. Considering cell segmentation problem, which plays a significant role in the analysis, the spatial properties of the data can be captured using Convolutional Neural Networks (CNNs). Recent approaches show promising segmentation results using convolutional encoder-decoders such as the U-Net. Nevertheless, these methods are limited by their inability to incorporate temporal information, that can facilitate segmentation of individual touching cells or of cells that are partially visible. In order to accommodate cell dynamics we propose a novel segmentation approach which integrates Convolutional Long Short Term Memory (C-LSTM) with the U-Net. The network's unique architecture allows it to capture multi-scale, compact, spatio-temporal encoding in the C-LSTMs memory units. Promising results, surpassing the state-of-the-art, are presented. The code is freely available at: TBD