Search Results for author:
Found 15 papers, 7 papers with code
WAIT: Feature Warping for Animation to Illustration video Translation using GANs
Current state-of-the-art video-to-video translation models rely on having a video sequence or a single style image to stylize an input video.
HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness
First, inspired by these observations, we propose a simple yet effective data augmentation method HybridAugment that reduces the reliance of CNNs on high-frequency components, and thus improves their robustness while keeping their clean accuracy high.
Improving Sketch Colorization using Adversarial Segmentation Consistency
We leverage semantic image segmentation from a general-purpose panoptic segmentation network to generate an additional adversarial loss function.
How Robust are Discriminatively Trained Zero-Shot Learning Models?
In this paper, we present novel analyses on the robustness of discriminative ZSL to image corruptions.
Adversarial Segmentation Loss for Sketch Colorization
Our loss function can be integrated to any baseline GAN model.
Red Carpet to Fight Club: Partially-supervised Domain Transfer for Face Recognition in Violent Videos
To mimic such scenarios, we formulate a realistic domain-transfer problem, where the goal is to transfer the recognition model trained on clean posed images to the target domain of violent videos, where training videos are available only for a subset of subjects.
A Deep Dive into Adversarial Robustness in Zero-Shot Learning
In constrast, Zero-shot Learning (ZSL) and Generalized Zero-shot Learning (GZSL) tasks inherently lack supervision across all classes.
GANILLA: Generative Adversarial Networks for Image to Illustration Translation
To address this problem, we propose a new framework for the quantitative evaluation of image-to-illustration models, where both content and style are taken into account using separate classifiers.
Wildest Faces: Face Detection and Recognition in Violent Settings
With the introduction of large-scale datasets and deep learning models capable of learning complex representations, impressive advances have emerged in face detection and recognition tasks.
DRAW: Deep networks for Recognizing styles of Artists Who illustrate children's books
The style was noticeable in other characters of the same illustrator in different books as well.
FAME: Face Association through Model Evolution
We attack the problem of learning face models for public faces from weakly-labelled images collected from web through querying a name.
Classifying Fonts and Calligraphy Styles Using Complex Wavelet Transform
Recognizing fonts has become an important task in document analysis, due to the increasing number of available digital documents in different fonts and emphases.
What is usual in unusual videos? Trajectory snippet histograms for discovering unusualness
Unusual events are important as being possible indicators of undesired consequences.
ConceptVision: A Flexible Scene Classification Framework
We introduce ConceptVision, a method that aims for high accuracy in categorizing large number of scenes, while keeping the model relatively simpler and efficient for scalability.
Rectifying Self Organizing Maps for Automatic Concept Learning from Web Images
The proposed method outperforms the state-of-the-art studies on the task of learning low-level concepts, and it is competitive in learning higher level concepts as well.
