22 research outputs found
X-CapsNet For Fake News Detection
News consumption has significantly increased with the growing popularity and
use of web-based forums and social media. This sets the stage for misinforming
and confusing people. To help reduce the impact of misinformation on users'
potential health-related decisions and other intents, it is desired to have
machine learning models to detect and combat fake news automatically. This
paper proposes a novel transformer-based model using Capsule neural
Networks(CapsNet) called X-CapsNet. This model includes a CapsNet with dynamic
routing algorithm paralyzed with a size-based classifier for detecting short
and long fake news statements. We use two size-based classifiers, a Deep
Convolutional Neural Network (DCNN) for detecting long fake news statements and
a Multi-Layer Perceptron (MLP) for detecting short news statements. To resolve
the problem of representing short news statements, we use indirect features of
news created by concatenating the vector of news speaker profiles and a vector
of polarity, sentiment, and counting words of news statements. For evaluating
the proposed architecture, we use the Covid-19 and the Liar datasets. The
results in terms of the F1-score for the Covid-19 dataset and accuracy for the
Liar dataset show that models perform better than the state-of-the-art
baselines
Online multiple people tracking-by-detection in crowded scenes
Multiple people detection and tracking is a challenging task in real-world crowded scenes. In this paper, we have presented an online multiple people tracking-by-detection approach with a single camera. We have detected objects with deformable part models and a visual background extractor. In the tracking phase we have used a combination of support vector machine (SVM) person-specific classifiers, similarity scores, the Hungarian algorithm and inter-object occlusion handling. Detections have been used for training person-specific classifiers and to help guide the trackers by computing a similarity score based on them and spatial information and assigning them to the trackers with the Hungarian algorithm. To handle inter-object occlusion we have used explicit occlusion reasoning. The proposed method does not require prior training and does not impose any constraints on environmental conditions. Our evaluation showed that the proposed method outperformed the state of the art approaches by 10% and 15% or achieved comparable performance
Identity-preserving Editing of Multiple Facial Attributes by Learning Global Edit Directions and Local Adjustments
Semantic facial attribute editing using pre-trained Generative Adversarial
Networks (GANs) has attracted a great deal of attention and effort from
researchers in recent years. Due to the high quality of face images generated
by StyleGANs, much work has focused on the StyleGANs' latent space and the
proposed methods for facial image editing. Although these methods have achieved
satisfying results for manipulating user-intended attributes, they have not
fulfilled the goal of preserving the identity, which is an important challenge.
We present ID-Style, a new architecture capable of addressing the problem of
identity loss during attribute manipulation. The key components of ID-Style
include Learnable Global Direction (LGD), which finds a shared and semi-sparse
direction for each attribute, and an Instance-Aware Intensity Predictor (IAIP)
network, which finetunes the global direction according to the input instance.
Furthermore, we introduce two losses during training to enforce the LGD to find
semi-sparse semantic directions, which along with the IAIP, preserve the
identity of the input instance. Despite reducing the size of the network by
roughly 95% as compared to similar state-of-the-art works, it outperforms
baselines by 10% and 7% in Identity preserving metric (FRS) and average
accuracy of manipulation (mACC), respectively
Multi Sentence Description of Complex Manipulation Action Videos
Automatic video description requires the generation of natural language
statements about the actions, events, and objects in the video. An important
human trait, when we describe a video, is that we are able to do this with
variable levels of detail. Different from this, existing approaches for
automatic video descriptions are mostly focused on single sentence generation
at a fixed level of detail. Instead, here we address video description of
manipulation actions where different levels of detail are required for being
able to convey information about the hierarchical structure of these actions
relevant also for modern approaches of robot learning. We propose one hybrid
statistical and one end-to-end framework to address this problem. The hybrid
method needs much less data for training, because it models statistically
uncertainties within the video clips, while in the end-to-end method, which is
more data-heavy, we are directly connecting the visual encoder to the language
decoder without any intermediate (statistical) processing step. Both frameworks
use LSTM stacks to allow for different levels of description granularity and
videos can be described by simple single-sentences or complex multiple-sentence
descriptions. In addition, quantitative results demonstrate that these methods
produce more realistic descriptions than other competing approaches