Introducing Geometry in Active Learning for Image Segmentation

We propose an Active Learning approach to training a segmentation classifier that exploits geometric priors to streamline the annotation process in 3D image volumes. To this end, we use these priors not only to select voxels most in need of annotation but to guarantee that they lie on 2D planar patch, which makes it much easier to annotate than if they were randomly distributed in the volume. A simplified version of this approach is effective in natural 2D images. We evaluated our approach on Electron Microscopy and Magnetic Resonance image volumes, as well as on natural images. Comparing our approach against several accepted baselines demonstrates a marked performance increase

Learning Active Learning from Data

In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection procedure as a regression problem we are not restricted to working with existing AL heuristics; instead, we learn strategies based on experience from previous AL outcomes. We show that a strategy can be learnt either from simple synthetic 2D datasets or from a subset of domain-specific data. Our method yields strategies that work well on real data from a wide range of domains

Learning Intelligent Dialogs for Bounding Box Annotation

We introduce Intelligent Annotation Dialogs for bounding box annotation. We train an agent to automatically choose a sequence of actions for a human annotator to produce a bounding box in a minimal amount of time. Specifically, we consider two actions: box verification, where the annotator verifies a box generated by an object detector, and manual box drawing. We explore two kinds of agents, one based on predicting the probability that a box will be positively verified, and the other based on reinforcement learning. We demonstrate that (1) our agents are able to learn efficient annotation strategies in several scenarios, automatically adapting to the image difficulty, the desired quality of the boxes, and the detector strength; (2) in all scenarios the resulting annotation dialogs speed up annotation compared to manual box drawing alone and box verification alone, while also outperforming any fixed combination of verification and drawing in most scenarios; (3) in a realistic scenario where the detector is iteratively re-trained, our agents evolve a series of strategies that reflect the shifting trade-off between verification and drawing as the detector grows stronger.Comment: This paper appeared at CVPR 201

Learning to Reduce Annotation Load

Modern machine learning methods and their applications in computer vision are known to crave for large amounts of training data to reach their full potential. Because training data is mostly obtained through humans who manually label samples, it induces a significant cost. Therefore, the problem of reducing the annotation load is of great importance for the success of machine learning methods. We study the problem of reducing the annotation load from two viewpoints, by answering the questions âWhat to annotate?â and âHow to annotate?â. The question âWhat?â addresses the selection of a small portion of the data that would be sufficient to train an accurate model. The question âHow? focuses on minimising the effort of labelling each datapoint. The question âWhat to annotate?â becomes particularly compelling if we can select data to be annotated in an iterative and adaptive way, a setting known as active learning (AL). The key challenge in AL is to identify the datapoints that are the most informative for the model at a given stage. We propose several techniques to address this challenge. Firstly, we consider the problem of segmenting natural images and image volumes. We take advantage of image priors, such as smoothness of objects of interest, and use them in a novel form of geometric uncertainty. Using this, we design an AL technique to efficiently annotate data that is tailored to segmentation applications. Next, we notice that no single manually-designed strategy outperforms others in every application and that often the burden of designing new strategies outweighs the benefits of AL. To overcome this problem we suggest learning an AL strategy from data by formulating the AL problem as a regression task that predicts the reduction in the generalisation error achieved by labelling each datapoint. This enables us to learn AL strategies from simulated data and to transfer them to new datasets. Finally, we turn towards non-myopic data-driven AL strategies. To this end, we formulate the AL problem as a Markov decision process and find the best selection policy using reinforcement learning. We design the decision process such that the policy can be learnt for any ML model and transferred to diverse application domains. Effectively addressing the question âHow to annotate?â is of no less importance as large cost savings can be achieved by labelling each datapoint more efficiently. This can be done with intelligent interfaces that interact with a human annotator. We make two contributions towards answering the question âHow?â. Firstly, we propose an efficient technique to annotate 3D image volumes for image segmentation. Annotating data in 3D is cumbersome and an obvious way to facilitate it is to select a subset of the data lying on a 2D plane. To find the optimal plane (i.e. the one containing the most informative datapoints) we design a branch-and-bound algorithm that quickly eliminates hypotheses about the optimal projection. Secondly, we propose an intelligent data annotation method to train object detectors. Instead of always asking the human annotator to draw bounding boxes in images, we detect automatically in which cases we can rely on the current detector and verify its proposal

ImSe : Instant Interactive Image Retrieval System with Exploration/Exploitation trade-off

Imagine a journalist looking for an illustration to his article about patriotism in a database of unannotated images. The idea of a suitable image is very vague and the best way to navigate through the database is to provide feedback to the images proposed by an Image Retrieval system in order to enable the system to learn what the ideal target image of the user is. Thus, at each search iteration a set of n images is displayed and the user must indicate how relevant they are to his/her target. When considering real-life problems we must also take into account the system's time-complexity and scalability to work with Big Data. To tackle this issue we utilize hierarchical Gaussian Process Bandits with visual Self-Organizing Map as a preprocessing technique. A prototype system called ImSe was developed and tested in experiments with real users in different types of tasks. The experiments show favorable results and indicate the benefits of proposed algorithms in different types of tasks

π2vec\pi2\text{vec}: Policy Representations with Successor Features

This paper describes $\pi2\text{vec}$, a method for representing behaviors of black box policies as feature vectors. The policy representations capture how the statistics of foundation model features change in response to the policy behavior in a task agnostic way, and can be trained from offline data, allowing them to be used in offline policy selection. This work provides a key piece of a recipe for fusing together three modern lines of research: Offline policy evaluation as a counterpart to offline RL, foundation models as generic and powerful state representations, and efficient policy selection in resource constrained environments.Comment: Accepted paper at ICLR202

Solonetzic soilscapes in the northern Caspian Lowland: local and spatial heterogeneity of pedofeatures and their changes in time

A comparative micromorphological analysis of soil fabrics within two- and three-component semidesert solonetz complexes formed within closed-drainage plains with different types of microtopography in the northern of the Caspian Lowland has been conducted. Micromorphological features of soils of most extensively studied three-component solonetz complexes having soil cover with a high degree of contrast are indicative of divergent evolutionary trends during the past half century. On the basis of these features, different directions of degradation of solonetzic and saline horizons are revealed. Although degradation of solonetz horizons is practically undetectable at a macro-scale, its micromorphological features are clearly manifested in parameters of aggregates, characteristics of coatings and types of salt pedofeatures. Common factors of solonetz evolution in nearby regions allow extrapolation of the micromorphological indicators of solonetz degradation to a wide range of objects including two-component solonetz complexes

Salinization dynamics in irrigated soils of the Svetloyarsk irrigation system, Volgograd oblast

On the basis of soil surveys performed by the Volgograd hydrogeological reclamation expedition in 1998 and 2006, published data, and original materials obtained by the authors, the dynamics of soil salinization within the Svetloyarsk irrigation system in Volgograd oblast during the irrigation and post-irrigation periods have been traced. It is found that high irrigation rates under conditions of poor drainage and closed drainage basins upon both shallow (within the Caspian Lowland) and relatively deep (on the Ergeni Upland) occurrence of saline groundwater and the presence of natural salts in the soils and subsoils lead to the rise in the groundwater level above the critical level and the development of secondary salinization in the previously surfacesaline, deeply saline, and even nonsaline soils. During the post-irrigation period (15-18 years) under modern climatic conditions, the groundwater level has been descending to a depth of more than 3 m, and the degree of salinity in the upper meter of light chestnut and meadow-chestnut soils has decreased owing to the leaching of salts with atmospheric precipitation