1,060 research outputs found
Zero-Annotation Object Detection with Web Knowledge Transfer
Object detection is one of the major problems in computer vision, and has
been extensively studied. Most of the existing detection works rely on
labor-intensive supervision, such as ground truth bounding boxes of objects or
at least image-level annotations. On the contrary, we propose an object
detection method that does not require any form of human annotation on target
tasks, by exploiting freely available web images. In order to facilitate
effective knowledge transfer from web images, we introduce a multi-instance
multi-label domain adaption learning framework with two key innovations. First
of all, we propose an instance-level adversarial domain adaptation network with
attention on foreground objects to transfer the object appearances from web
domain to target domain. Second, to preserve the class-specific semantic
structure of transferred object features, we propose a simultaneous transfer
mechanism to transfer the supervision across domains through pseudo strong
label generation. With our end-to-end framework that simultaneously learns a
weakly supervised detector and transfers knowledge across domains, we achieved
significant improvements over baseline methods on the benchmark datasets.Comment: Accepted in ECCV 201
Structure propagation for zero-shot learning
The key of zero-shot learning (ZSL) is how to find the information transfer
model for bridging the gap between images and semantic information (texts or
attributes). Existing ZSL methods usually construct the compatibility function
between images and class labels with the consideration of the relevance on the
semantic classes (the manifold structure of semantic classes). However, the
relationship of image classes (the manifold structure of image classes) is also
very important for the compatibility model construction. It is difficult to
capture the relationship among image classes due to unseen classes, so that the
manifold structure of image classes often is ignored in ZSL. To complement each
other between the manifold structure of image classes and that of semantic
classes information, we propose structure propagation (SP) for improving the
performance of ZSL for classification. SP can jointly consider the manifold
structure of image classes and that of semantic classes for approximating to
the intrinsic structure of object classes. Moreover, the SP can describe the
constrain condition between the compatibility function and these manifold
structures for balancing the influence of the structure propagation iteration.
The SP solution provides not only unseen class labels but also the relationship
of two manifold structures that encode the positive transfer in structure
propagation. Experimental results demonstrate that SP can attain the promising
results on the AwA, CUB, Dogs and SUN databases
Effect of excited states and applied magnetic fields on the measured hole mobility in an organic semiconductor
Copyright 2010 by the American Physical Society. Article is available at
The kinetic description of vacuum particle creation in the oscillator representation
The oscillator representation is used for the non-perturbative description of
vacuum particle creation in a strong time-dependent electric field in the
framework of scalar QED. It is shown that the method can be more effective for
the derivation of the quantum kinetic equation (KE) in comparison with the
Bogoliubov method of time-dependent canonical transformations. This KE is used
for the investigation of vacuum creation in periodical linear and circular
polarized electric fields and also in the case of the presence of a constant
magnetic field, including the back reaction problem. In particular, these
examples are applied for a model illustration of some features of vacuum
creation of electron-positron plasma within the planned experiments on the
X-ray free electron lasers.Comment: 17 pages, 3 figures, v2: a reference added; some changes in tex
Charge carrier injection into insulating media: single-particle versus mean-field approach
Self-consistent, mean-field description of charge injection into a dielectric
medium is modified to account for discreteness of charge carriers. The improved
scheme includes both the Schottky barrier lowering due to the individual image
charge and the barrier change due to the field penetration into the injecting
electrode that ensures validity of the model at both high and low injection
rates including the barrier dominated and the space-charge dominated regimes.
Comparison of the theory with experiment on an unipolar ITO/PPV/Au-device is
presented.Comment: 32 pages, 9 figures; revised version accepted to PR
A Deep Dive into Adversarial Robustness in Zero-Shot Learning
Machine learning (ML) systems have introduced significant advances in various
fields, due to the introduction of highly complex models. Despite their
success, it has been shown multiple times that machine learning models are
prone to imperceptible perturbations that can severely degrade their accuracy.
So far, existing studies have primarily focused on models where supervision
across all classes were available. In constrast, Zero-shot Learning (ZSL) and
Generalized Zero-shot Learning (GZSL) tasks inherently lack supervision across
all classes. In this paper, we present a study aimed on evaluating the
adversarial robustness of ZSL and GZSL models. We leverage the well-established
label embedding model and subject it to a set of established adversarial
attacks and defenses across multiple datasets. In addition to creating possibly
the first benchmark on adversarial robustness of ZSL models, we also present
analyses on important points that require attention for better interpretation
of ZSL robustness results. We hope these points, along with the benchmark, will
help researchers establish a better understanding what challenges lie ahead and
help guide their work.Comment: To appear in ECCV 2020, Workshop on Adversarial Robustness in the
Real Worl
Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
Deep learning for regression tasks on medical imaging data has shown
promising results. However, compared to other approaches, their power is
strongly linked to the dataset size. In this study, we evaluate
3D-convolutional neural networks (CNNs) and classical regression methods with
hand-crafted features for survival time regression of patients with high grade
brain tumors. The tested CNNs for regression showed promising but unstable
results. The best performing deep learning approach reached an accuracy of
51.5% on held-out samples of the training set. All tested deep learning
experiments were outperformed by a Support Vector Classifier (SVC) using 30
radiomic features. The investigated features included intensity, shape,
location and deep features. The submitted method to the BraTS 2018 survival
prediction challenge is an ensemble of SVCs, which reached a cross-validated
accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set,
and 42.9% on the testing set. The results suggest that more training data is
necessary for a stable performance of a CNN model for direct regression from
magnetic resonance images, and that non-imaging clinical patient information is
crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation
(BraTS) Challenge 2018, survival prediction tas
Invertible Zero-Shot Recognition Flows
© 2020, Springer Nature Switzerland AG. Deep generative models have been successfully applied to Zero-Shot Learning (ZSL) recently. However, the underlying drawbacks of GANs and VAEs (e.g., the hardness of training with ZSL-oriented regularizers and the limited generation quality) hinder the existing generative ZSL models from fully bypassing the seen-unseen bias. To tackle the above limitations, for the first time, this work incorporates a new family of generative models (i.e., flow-based models) into ZSL. The proposed Invertible Zero-shot Flow (IZF) learns factorized data embeddings (i.e., the semantic factors and the non-semantic ones) with the forward pass of an invertible flow network, while the reverse pass generates data samples. This procedure theoretically extends conventional generative flows to a factorized conditional scheme. To explicitly solve the bias problem, our model enlarges the seen-unseen distributional discrepancy based on a negative sample-based distance measurement. Notably, IZF works flexibly with either a naive Bayesian classifier or a held-out trainable one for zero-shot recognition. Experiments on widely-adopted ZSL benchmarks demonstrate the significant performance gain of IZF over existing methods, in both classic and generalized settings
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