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Found 60 papers, 13 papers with code
MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation
This approach effectively synergizes reference image and text prompt information to produce valuable image features, facilitating an image diffusion model.
AI Art Neural Constellation: Revealing the Collective and Contrastive State of AI-Generated and Human Art
Our comparative analysis is based on an extensive dataset, dubbed ``ArtConstellation,'' consisting of annotations about art principles, likability, and emotions for 6, 000 WikiArt and 3, 200 AI-generated artworks.
Diffusion Guided Domain Adaptation of Image Generators
Can a text-to-image diffusion model be used as a training objective for adapting a GAN generator to another domain?
Formal Analysis of Art: Proxy Learning of Visual Concepts from Style Through Language Models
In this paper, we propose a novel proxy model and reformulate four pre-existing methods in the context of proxy learning.
PIVQGAN: Posture and Identity Disentangled Image-to-Image Translation via Vector Quantization
The VQSN module facilitates a more delicate separation of posture and identity, while the training scheme ensures the VQSN module learns the pose-related representations.
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images.
Ranked #2 on
Image Generation
on ADE-Indoor
Self-Supervised Sketch-to-Image Synthesis
Moreover, with the proposed sketch generator, the model shows a promising performance on style mixing and style transfer, which require synthesized images to be both style-consistent and semantically meaningful.
Spatial Frequency Bias in Convolutional Generative Adversarial Networks
As the success of Generative Adversarial Networks (GANs) on natural images quickly propels them into various real-life applications across different domains, it becomes more and more important to clearly understand their limitations.
TIME: Text and Image Mutual-Translation Adversarial Networks
Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I generator G and an image captioning discriminator D under the Generative Adversarial Network framework.
Offensive language detection in Arabic using ULMFiT
In this paper, we approach the shared task OffenseEval 2020 by Mubarak et al. (2020) using ULMFiT Howard and Ruder (2018) pre-trained on Arabic Wikipedia Khooli (2019) which we use as a starting point and use the target data-set to fine-tune it.
ULMFiT replication
Authors: Mohamed Abdellatif and Ahmed Elgammal Gitlab URL: https://gitlab. com/abdollatif/lrec{\_}app Commit hash: 3f20b2ddb96d8c865e5f56f5566edf371214785f Tag name: Splits2 Dataset file md5: 5aee3dac5e48d1ac3d279083212734c9 Dataset URL: https://drive. google. com/file/d/1cv5HuQhgFVizupFI40dzreemS2gMM498/view? usp=sharing
Sketch-to-Art: Synthesizing Stylized Art Images From Sketches
We propose a new approach for synthesizing fully detailed art-stylized images from sketches.
2nd Place Solution to the GQA Challenge 2019
We present a simple method that achieves unexpectedly superior performance for Complex Reasoning involved Visual Question Answering.
OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization
Exploring the potential of GANs for unsupervised disentanglement learning, this paper proposes a novel GAN-based disentanglement framework with One-Hot Sampling and Orthogonal Regularization (OOGAN).
Learning Feature-to-Feature Translator by Alternating Back-Propagation for Generative Zero-Shot Learning
We investigate learning feature-to-feature translator networks by alternating back-propagation as a general-purpose solution to zero-shot learning (ZSL) problems.
Graphical Contrastive Losses for Scene Graph Parsing
The first, Entity Instance Confusion, occurs when the model confuses multiple instances of the same type of entity (e. g. multiple cups).
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes.
An Interpretable Model for Scene Graph Generation
We propose an efficient and interpretable scene graph generator.
Introduction to the 1st Place Winning Model of OpenImages Relationship Detection Challenge
This article describes the model we built that achieved 1st place in the OpenImage Visual Relationship Detection Challenge on Kaggle.
Disconnected Manifold Learning for Generative Adversarial Networks
Natural images may lie on a union of disjoint manifolds rather than one globally connected manifold, and this can cause several difficulties for the training of common Generative Adversarial Networks (GANs).
Large-Scale Visual Relationship Understanding
Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of <subject, relation, object> triples.
A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts
Most existing zero-shot learning methods consider the problem as a visual semantic embedding one.
Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication
We also propose and compare different classification methods at the drawing level.
Link the head to the "beak": Zero Shot Learning from Noisy Text Description at Part Precision
We propose a learning framework that is able to connect text terms to its relevant parts and suppress connections to non-visual text terms without any part-text annotations.
A Multilayer-Based Framework for Online Background Subtraction with Freely Moving Cameras
The exponentially increasing use of moving platforms for video capture introduces the urgent need to develop the general background subtraction algorithms with the capability to deal with the moving background.
Relationship Proposal Networks
We demonstrate the ability of our Rel-PN to localize relationships with only a few thousand proposals.
CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms
We argue that such networks are limited in their ability to generate creative products in their original design.
Overlapping Cover Local Regression Machines
We present the Overlapping Domain Cover (ODC) notion for kernel machines, as a set of overlapping subsets of the data that covers the entire training set and optimized to be spatially cohesive as possible.
Modelling depth for nonparametric foreground segmentation using RGBD devices
In order to unify all the device channel cues, a new probabilistic depth data model is also proposed where we show how handle the inaccurate data to improve foreground segmentation.
SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition
In this paper, we propose a new CNN architecture that integrates semantic part detection and abstraction (SPDA-CNN) for fine-grained classification.
Automatic Annotation of Structured Facts in Images
Motivated by the application of fact-level image understanding, we present an automatic method for data collection of structured visual facts from images with captions.
The Role of Typicality in Object Classification: Improving The Generalization Capacity of Convolutional Neural Networks
We focus on Convolutional Neural Networks (CNN) as the state-of-the-art models in object recognition and classification; investigate this problem in more detail, and hypothesize that training CNN models suffer from unstructured loss minimization.
Manifold-Kernels Comparison in MKPLS for Visual Speech Recognition
Embedding the visual units on a manifold and using manifold kernels is one way to measure these distances.
Learning Kernels for Structured Prediction using Polynomial Kernel Transformations
In this work we focus on learning kernel representations for structured regression.
Supervised Dimensionality Reduction via Distance Correlation Maximization
In our work, we propose a novel formulation for supervised dimensionality reduction based on a nonlinear dependency criterion called Statistical Distance Correlation, Szekely et.
Write a Classifier: Predicting Visual Classifiers from Unstructured Text
Then, we propose a new constrained optimization formulation that combines a regression function and a knowledge transfer function with additional constraints to predict the parameters of a linear classifier.
Toward a Taxonomy and Computational Models of Abnormalities in Images
In this paper we study various types of atypicalities in images in a more comprehensive way than has been done before.
Zero-Shot Event Detection by Multimodal Distributional Semantic Embedding of Videos
To our knowledge, this is the first Zero-Shot event detection model that is built on top of distributional semantics and extends it in the following directions: (a) semantic embedding of multimodal information in videos (with focus on the visual modalities), (b) automatically determining relevance of concepts/attributes to a free text query, which could be useful for other applications, and (c) retrieving videos by free text event query (e. g., "changing a vehicle tire") based on their content.
Convolutional Models for Joint Object Categorization and Pose Estimation
In the task of Object Recognition, there exists a dichotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant representation, while the latter requires a representation capable of capturing pose information over different categories of objects.
Sherlock: Scalable Fact Learning in Images
We show that learning visual facts in a structured way enables not only a uniform but also generalizable visual understanding.
Digging Deep into the layers of CNNs: In Search of How CNNs Achieve View Invariance
How does fine-tuning of a pre-trained CNN on a multi-view dataset affect the representation at each layer of the network?
Tell and Predict: Kernel Classifier Prediction for Unseen Visual Classes from Unstructured Text Descriptions
In this paper we propose a framework for predicting kernelized classifiers in the visual domain for categories with no training images where the knowledge comes from textual description about these categories.
Quantifying Creativity in Art Networks
The proposed computational framework is based on constructing a network between creative products and using this network to infer about the originality and influence of its nodes.
Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature
In the past few years, the number of fine-art collections that are digitized and publicly available has been growing rapidly.
Factorization of View-Object Manifolds for Joint Object Recognition and Pose Estimation
Due to large variations in shape, appearance, and viewing conditions, object recognition is a key precursory challenge in the fields of object manipulation and robotic/AI visual reasoning in general.
Learning Hypergraph-regularized Attribute Predictors
Then the attribute prediction problem is casted as a regularized hypergraph cut problem in which HAP jointly learns a collection of attribute projections from the feature space to a hypergraph embedding space aligned with the attribute space.
Abnormal Object Recognition: A Comprehensive Study
When describing images, humans tend not to talk about the obvious, but rather mention what they find interesting.
On The Effect of Hyperedge Weights On Hypergraph Learning
However, many studies on pairwise graphs show that the choice of edge weight can significantly influence the performances of such graph algorithms.
Computational Beauty: Aesthetic Judgment at the Intersection of Art and Science
Through an investigation of the ethical consequences of this innovative technology, the unquestioned authority of the art expert is challenged and the subjective nature of aesthetic judgment is brought to philosophical scrutiny once again.
Generalized Twin Gaussian Processes using Sharma-Mittal Divergence
In this paper, we present a generalized structured regression framework based on Shama-Mittal divergence, a relative entropy measure, which is introduced to the Machine Learning community in this work.
Toward Automated Discovery of Artistic Influence
The contribution of this paper is in exploring the problem of computer-automated suggestion of influences between artists, a problem that was not addressed before in a general setting.
Text to Multi-level MindMaps: A Novel Method for Hierarchical Visual Abstraction of Natural Language Text
This work firstly introduces MindMap Multilevel Visualization concept which is to jointly visualize and summarize textual information.
Simultaneous Twin Kernel Learning using Polynomial Transformations for Structured Prediction
We formulate this problem for learning covariances kernels of Twin Gaussian Processes.
Collaborative Discriminant Locality Preserving Projections With its Application to Face Recognition
In our algorithm, the discriminating power of DLPP are further exploited from two aspects.
Visual-Semantic Scene Understanding by Sharing Labels in a Context Network
We consider the problem of naming objects in complex, natural scenes containing widely varying object appearance and subtly different names.
DISCOMAX: A Proximity-Preserving Distance Correlation Maximization Algorithm
Our setting is different from subset-selection algorithms where the problem is to choose the best subset of features for regression.
Object-Centric Anomaly Detection by Attribute-Based Reasoning
When describing images, humans tend not to talk about the obvious, but rather mention what they find interesting.
MKPLS: Manifold Kernel Partial Least Squares for Lipreading and Speaker Identification
Our approach outperforms for the speaker semi-dependent setting by at least 15% of the baseline, and competes in the other two settings.
