Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation

We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to expand objects based on the information about which classes can occur in an image, and to constrain the segmentations to coincide with object boundaries. We show experimentally that training a deep convolutional neural network using the proposed loss function leads to substantially better segmentations than previous state-of-the-art methods on the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the working mechanism of our method by a detailed experimental study that illustrates how the segmentation quality is affected by each term of the proposed loss function as well as their combinations.Comment: ECCV 201

Superpixel Convolutional Networks using Bilateral Inceptions

In this paper we propose a CNN architecture for semantic image segmentation. We introduce a new 'bilateral inception' module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image. The feature spaces for bilateral filtering and other parameters of the module are learned end-to-end using standard backpropagation techniques. The bilateral inception module addresses two issues that arise with general CNN segmentation architectures. First, this module propagates information between (super) pixels while respecting image edges, thus using the structured information of the problem for improved results. Second, the layer recovers a full resolution segmentation result from the lower resolution solution of a CNN. In the experiments, we modify several existing CNN architectures by inserting our inception module between the last CNN (1x1 convolution) layers. Empirical results on three different datasets show reliable improvements not only in comparison to the baseline networks, but also in comparison to several dense-pixel prediction techniques such as CRFs, while being competitive in time.Comment: European Conference on Computer Vision (ECCV), 201

3 ns single-shot read-out in a quantum dot-based memory structure

This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Appl. Phys. Lett. 104, 053111 (2014) and may be found at https://doi.org/10.1063/1.4864281.Fast read-out of two to six charges per dot from the ground and first excited state in a quantum dot (QD)-based memory is demonstrated using a two-dimensional electron gas. Single-shot measurements on modulation-doped field-effect transistor structures with embedded InAs/GaAs QDs show read-out times as short as 3 ns. At low temperature (T = 4.2 K) this read-out time is still limited by the parasitics of the setup and the device structure. Faster read-out times and a larger read-out signal are expected for an improved setup and device structure

Materials for Future Quantum Dot-Based Memories

The present paper investigates the current status of the storage times in self-organized QDs, surveying a variety of heterostructures advantageous for strong electron and/or hole confinement. Experimental data for the electronic properties, such as localization energies and capture cross-sections, are listed. Based on the theory of thermal emission of carriers from QDs, we extrapolate the values for materials that would increase the storage time at room temperature to more than millions of years. For electron storage, GaSb/AlSb, GaN/AlN, and InAs/AlSb are proposed. For hole storage, GaSb/Al0.9Ga0.1As, GaSb/GaP, and GaSb/AlP are promising candidates

A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems

International audienceSzeliski et al. published an influential study in 2006 on energy minimization methods for Markov Random Fields (MRF). This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically , the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different car-dinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2,453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types

A Two-Dimensional Electron Gas as a Sensitive Detector for Time-Resolved Tunneling Measurements on Self-Assembled Quantum Dots

A two-dimensional electron gas (2DEG) situated nearby a single layer of self-assembled quantum dots (QDs) in an inverted high electron mobility transistor (HEMT) structure is used as a detector for time-resolved tunneling measurements. We demonstrate a strong influence of charged QDs on the conductance of the 2DEG which allows us to probe the tunneling dynamics between the 2DEG and the QDs time resolved. Measurements of hysteresis curves with different sweep times and real-time conductance measurements in combination with an boxcar-like evaluation method enables us to unambiguously identify the transients as tunneling events between the s- and p-electron QD states and the 2DEG and rule out defect-related transients

Linking structural and electronic properties of high-purity self-assembled GaSb/GaAs quantum dots

We present structural, electrical, and theoretical investigations of self-assembled type-II GaSb/GaAs quantum dots (QDs) grown by molecular beam epitaxy. Using cross-sectional scanning tunneling microscopy (X-STM) the morphology of the QDs is determined. The QDs are of high purity (similar to 100% GaSb content) and have most likely the shape of a truncated pyramid. The average heights of the QDs are 4-6 nm with average base lengths between 9 and 14 nm. Samples with a QD layer embedded into a pn-diode structure are studied with deep-level transient spectroscopy (DLTS), yielding a hole localization energy in the QDs of 609 meV. Based on the X-STM results the electronic structure of the QDs is calculated using 8-band k.p theory. The theoretical localization energies are found to be in good agreement with the DLTS results. Our results also allow us to estimate how variations in size and shape of the dots influence the hole localization energy

gBoost: A Mathematical Programming Approach to Graph Classification and Regression

Graph mining methods enumerate frequently appearing subgraph patterns, which can be used as features for subsequent classification or regression. However, frequent patterns are not necessarily informative for the given learning problem. We propose a mathematical programming boosting method (gBoost) that progressively collects informative patterns. Compared to AdaBoost, gBoost can build the prediction rule with fewer iterations. To apply the boosting method to graph data, a branch-and-bound pattern search algorithm is developed based on the DFS code tree. The constructed search space is reused in later iterations to minimize the computation time. Our method can learn more efficiently than the simpler method based on frequent substructure mining, because the output labels are used as an extra information source for pruning the search space. Furthermore, by engineering the mathematical program, a wide range of machine learning problems can be solved without modifying the pattern search algorithm