Search By Subject

Anomaly detection refers to identifying abnormal images and localizing anomalous regions. Reconstruction-based anomaly detection is a commonly used method; however, traditional reconstruction-based methods perform poorly as deep models generalize ...

Existing video shadow detectors often need postprocessing or additional input to perform better, thereby degrading their video shadow detection speed. In this work, we present a novel spatial-temporal fusion network (STF-Net), which can efficiently ...

Observing human beings from monocular images is one of the basic tasks of computer vision. Reconstructing human bodies from monocular images mainly includes the reconstruction of posture and body shape. However, in the past studies, researchers were ...

Virtual reality (VR) technology has become a growing force in entertainment, education, science, and manufacturing due to the capability of providing users with immersive experiences and natural interaction. Although common input devices such as ...

Word-level sign language recognition (WSLR), which aims to translate a sign video into one word, serves as a fundamental task in visual sign language research. Existing WSLR methods focus on recognizing frontal view hand images, which may hurt ...

Video virtual try-on methods aim to generate coherent, smooth, and realistic try-on videos, it matches the target clothing with the person in the video in a spatiotemporally consistent manner. Existing methods can match the human body with the clothing ...

We introduce the Synthetic Pedestrian Dataset (SynPeDS) which was designed to support a systematic safety analysis for pedestrian detection tasks in urban scenes. The dataset was generated synthetically with a real-time and a physically-based rendering ...

Object detection is a matured technique, converging to the detection performance of human vision. This paper presents a method to further close the remaining gap of detection capability by investigating visual factors impairing the detectability of ...

Interactions based on automatic speech recognition (ASR) have become widely used, with speech input being increasingly utilized to create documents. However, as there is no easy way to distinguish between commands being issued and text required to be ...

We propose a text editor to help users plan, structure and reflect on their writing process. It provides continuously updated paragraph-wise summaries as margin annotations, using automatic text summarization. Summary levels range from full text, to ...

Despite the increasing popularity of VR games, one factor hindering the industry’s rapid growth is motion sickness experienced by the users. Symptoms such as fatigue and nausea severely hamper the user experience. Machine Learning methods could be used ...

The saying Garbage In, Garbage Out resonates perfectly within the machine learning and artificial intelligence community. While there has been considerable ongoing effort for improving the quality of models, there is relatively less focus on ...

Usually data scientists are adept in deriving valuable insights from data by applying appropriate machine learning algorithms. However, data scientists are usually not skilled in developing or operating production level software which is the domain of ...

This tutorial will elaborate on various available resources for the natural language generation (NLG) tasks in code-mixed languages. We will also discuss the adaptability, limitations, and challenges with various evaluation metrics for the code-mixed ...

Automatic scoring engines have been used for scoring approximately fifteen million test-takers in just the last three years. This number is increasing further due to COVID-19 and the associated automation of education and testing. Yet, the AI-based ...

In the current era, data are generated by almost every electronic devices. These data are often produced continuously from many IoT devices resulting in an enormous corpus of data streams. To learn and model such data streams, an adaptive, robust and ...

Deep Learning Models such as Convolution Neural Networks (CNNs) have shown great potential in various applications. However, these techniques will face regulatory compliance challenges related to privacy of user data, especially when they are deployed ...

Artificial Neural Networks (ANNs) have drawn academy and industry attention for their ability to represent and solve complex problems. Researchers are studying how to distribute their computation to reduce their training time. However, the most common ...

We consider the problem of achieving fair classification in Federated Learning (FL) under data heterogeneity. Most of the approaches proposed for fair classification require diverse data that represent the different demographic groups involved. In ...