Fashion is Taking Shape: Disentangled Person Image Generation L. Connecting Pixels to Privacy and Utility: Justifying Decisions and Pointing to the Evidence D. A Pedestrians at Night Dataset L.
While great progress has been made recently in automatic image manipulation, it has been limited to object centric images like faces or structured scene datasets. In this work, we take a step "Skulle du fa stryk" general scene-level image editing by developing an automatic interaction-free object removal model.
Our model learns to find and remove objects from general scene images using image-level labels "Skulle du fa stryk" unpaired data in a generative adversarial network GAN framework. We achieve this with two key contributions: We experimentally show on two datasets that our method effectively removes a wide variety of objects using weak supervision only.
Schulz Bioinformatics, Volume 34, Number 17, Which one is me? Identifying Oneself on Public Displays M. Bulling Computer Graphics Forum Proc. Grounding Visual Explanations L. Deep neural perception and control networks have become key com- ponents of self-driving vehicles. User acceptance is likely to benefit from easy- to-interpret textual explanations which allow end-users to understand what trig- gered a particular behavior.
We propose a new approach to introspective ex- planations which consists of two parts. First, we use a visual spatial attention model to train a convolutional network end-to-end from images to the vehicle control commands, i.
Second, we use an attention-based video-to-text model to produce textual ex- planations of model actions. The attention maps of controller and explanation model are aligned so that explanations are grounded in the parts of the scene that mattered to the controller. We explore two approaches to attention alignment, strong- and weak-alignment. Finally, we explore a version of our model that generates rationalizations, and compare with introspective explanations on the same video segments.
Code is available at https: Bulling Frontiers in Human Neuroscience, Volume 12, Van Gool and T. Due to the importance of zero-shot learning, i. We argue that it is time to take a step back and to analyze the status quo of the area.
The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task.
This is an important contribution as published results are often not comparable and sometimes even flawed due to, e. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 AWA2 dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting.
Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it. We enable our analysis by creating a human baseline for pedestrian detection over the Caltech pedestrian dataset.
After manually clustering the frequent errors of a top detector, we characterise both localisation and background- versus-foreground errors. To address localisation errors we study the impact of training annotation noise on the detector performance, and show that we can improve results even with a small portion of sanitised training data.
Other than our in-depth analysis, we report top performance on the Caltech pedestrian dataset, and provide a new sanitised set of training and test annotations. Geiger International Journal of Computer Vision, Menze Medical Image Analysis, Volume 48, Survey and Lessons Learned M. Natural language explanations of deep neural network decisions provide an intuitive way for a AI agent to articulate a reasoning process.
Current textual explanations learn to discuss class discriminative features in an image. However, it is also helpful to understand which attributes might change a classification decision if present in an image e.
To demonstrate our method we consider a fine-grained image classification task in which we take as input an image and a counterfactual class and output text which explains why the image does not belong to a counterfactual class. We then analyze our generated counterfactual explanations both qualitatively and quantitatively using proposed automatic metrics.
The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which opens doors for huge uncertainties. Since the microstructure could be a combination of different phases with complex substructures its automatic classification is very challenging and just a little work in this field has been carried out.
Prior related works apply mostly designed and engineered
Skulle du fa stryk by experts and classify microstructure separately from Skulle du fa stryk extraction step.
Recently Deep Learning methods have shown surprisingly good performance in vision applications by learning the features from data together with the classification step. In this work, we propose a deep learning method for microstructure classification in the examples of certain microstructural constituents of low carbon steel.
Our system achieves Beyond the success presented in this paper, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation. Theobalt Technical Report, The matching of multiple objects e. In order to robustly handle ambiguities, noise and repetitive patterns in challenging real-world settings, it is essential to take geometric Skulle du fa stryk between points into account.
Computationally, the multi-matching problem is difficult. It can be phrased as simultaneously solving multiple NP-hard quadratic assignment problems QAPs that are coupled via cycle-consistency constraints.
The main limitations of existing multi-matching methods are that they either ignore geometric consistency and thus have limited robustness, or they are restricted to small-scale problems due to their relatively high computational cost. We address these shortcomings by introducing a Higher-order Projected Power Iteration method, which is i efficient and scales to tens of thousands of "Skulle du fa stryk," ii straightforward to implement, iii able to incorporate geometric consistency, and iv guarantees cycle-consistent multi-matchings.
Experimentally we show that our approach is superior to existing methods. Schiele Technical Report, a. For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence.
This problem can be formalized as a sequence prediction problem, where a number of observations are used to predict the sequence into the future. However, real-world scenarios demand a model of uncertainty of such predictions, as future states become increasingly uncertain and multi-modal -- in Skulle du fa stryk on long time horizons. This makes modelling and learning challenging. We cast state of the art semantic segmentation and future prediction models based on deep learning into a Bayesian formulation that in turn allows for a full Bayesian treatment of the prediction problem.
We present a new sampling scheme for this model that draws from the success of variational autoencoders by incorporating a recognition network. In the experiments we show that our model outperforms prior work in accuracy of the predicted segmentation and provides calibrated probabilities that also better capture the multi-modal aspects of possible future states of street scenes.
Schiele Technical Report, b. For autonomous agents to successfully operate in the Skulle du fa stryk world, the ability to anticipate future scene states is a key competence. In real-world scenarios, future states become increasingly uncertain and multi-modal, particularly on long time horizons.
However, it turns out that such approaches fall short to capture complex real-world scenes, even falling behind in accuracy when compared to the plain deterministic approaches. This is because the used log-likelihood estimate discourages diversity. In this work, we propose a novel Bayesian formulation for anticipating future scene states which leverages synthetic likelihoods that encourage the learning of diverse models to accurately capture the multi-modal nature of future scene states.
We show that our approach achieves accurate state-of-the-art predictions and calibrated probabilities through extensive experiments for scene anticipation on Cityscapes dataset. Moreover, we show that our approach generalizes across diverse tasks such as digit generation and precipitation forecasting. Welling Technical Report, We introduce Primal-Dual Wasserstein GAN, a new learning algorithm for building latent variable models of the data distribution based on the primal and the dual formulations of the optimal
Skulle du fa stryk OT problem.
We utilize the primal formulation to learn a flexible inference mechanism and to create an optimal approximate coupling between the data distribution and the generative model. In order to learn the generative model, we use the dual formulation and train the decoder adversarially through a critic network that is regularized by the approximate coupling obtained from the primal. Unlike previous methods that violate various properties of the optimal critic, we regularize the norm and the direction "Skulle du fa stryk" the gradients of the critic function.
Our model shares many of the desirable properties of auto-encoding models in terms of mode coverage and latent structure, while avoiding their undesirable averaging properties, e.
Fritz Technical Report, With the widespread use of machine learning ML techniques, ML as a service has become increasingly popular.
In this setting, an ML model resides on a server and users can query the model with their data via an API. However, if the user's input is sensitive, sending it to the server is not an option.
Equally, the service provider does not want to share the model by sending it to the client for protecting its intellectual property and pay-per-query business model. In this paper, we propose MLCapsule, a guarded offline deployment of machine learning as a service.
MLCapsule executes the machine learning model locally on the user's client and therefore the data never leaves the client. Meanwhile, MLCapsule offers the service provider the same level of control and security of its model as the commonly used server-side execution. In addition, MLCapsule is applicable to offline applications that require local
Skulle du fa stryk.
Erdem Technical Report, In this study, we explore building a two-stage framework for enabling users to directly manipulate high-level attributes of a natural scene.
The key to our approach is a deep generative network which can hallucinate images of a scene as if they were taken at a different season e.
Once the scene is hallucinated with the given attributes, the corresponding look is then transferred to the input image while preserving the semantic "Skulle du fa stryk" intact, giving a photo-realistic manipulation result. As the proposed framework hallucinates what the scene will look like, it does not require any reference style image as commonly utilized in most of the appearance or style transfer approaches.XR Skull Complete,XR Small .PET Image-w/CT- Skull to Mid-Thigh,PETPM Du DRT ProMRI W/Cell HM, PMCATH ARTERIAL FEMORAL 18G FA- DRILL WIRE PASS MM STRYK
Skulle du fa stryk, popularmusicians.info popularmusicians.info popularmusicians.info //popularmusicians.infod .net/sv/nar-stroken-slar-till-ar-det-ofta-relationerna-som-far-ta-strykokt PhD Thesis, Universität des Saarlandes, SkullConduct: Biometric User Identification on Eyewear Computers Using Bone Conduction Through the Skull T.
Beier, B. Andres, U. Köthe and F. A. Hamprecht S. Kohlbrecher, K. Petersen, O. Schwahn, M. Andriluka, U. Klingauf, S. Roth, B. Schiele and O. von Stryk.