How does a pre-trained Convolution Neural Network (CNN) model perform on beauty and personal care items (i.e Perfect-500K) This is the question we attempt to answer in this paper by adopting several well known deep learning models pre-trained on ...

Training and evaluation of laparoscopic skills have become an important aspect of young surgeons' education. The evaluation process is currently performed manually by experienced surgeons through reviewing video recordings of laparoscopic procedures for ...

Due to the advances in deep reinforcement learning and the demand of large training data, virtual-to-real learning has gained lots of attention from computer vision community recently. As state-of-the-art 3D engines can generate photo-realistic images ...

Speechreading or lipreading is the technique of understanding and getting phonetic features from a speaker's visual features such as movement of lips, face, teeth and tongue. It has a wide range of multimedia applications such as in surveillance, ...

In this demo, we demonstrate "Real-time Food Category Change'' based on a Conditional Cycle GAN (cCycle GAN) with a large-scale food image data collected from the Twitter Stream. Conditional Cycle GAN is an extension of CycleGAN, which enables "Food ...

For many real-world multimedia applications, data are often described by multiple views. Therefore, multi-view learning researches are of great significance. Traditional multi-view clustering methods assume that each view has complete data. However, ...

Current researches mainly focus on single-view and multiview human action recognition, which can hardly satisfy the requirements of human-robot interaction (HRI) applications to recognize actions from arbitrary views. The lack of databases also sets up ...

In this paper, we present a method to generate realistic defensive plays in a basketball game based on the ball and the offensive team's movements. Our system allows players and coaches to simulate how the opposing team will react to a newly developed ...

Video captioning is a challenging task owing to the complexity of understanding the copious visual information in videos and describing it using natural language. Different from previous work that encodes video information using a single flow, in this ...

Local matching problems (e.g. key point matching, geometry registration) are significant but challenging tasks in computer vision field. In this paper, we propose to learn a robust local 3D descriptor from volumetric point patches to tackle the local ...

Using dropout in Visual Question Answering (VQA) is a common practice to prevent overfitting. However, in multi-path networks, the current way to use dropout may cause two problems: the co-adaptations of neurons and the explosion of output variance. In ...

Scribble colors based line art colorization is a challenging computer vision problem since neither greyscale values nor semantic information is presented in line arts, and the lack of authentic illustration-line art training pairs also increases ...

Modeling statistical regularity plays an essential role in ill-posed image processing problems. Recently, deep learning based methods have been presented to implicitly learn statistical representation of pixel distributions in natural images and ...

Multi-label image classification is a fundamental and challenging task in computer vision, and recently achieved significant progress by exploiting semantic relations among labels. However, the spatial positions of labels for multi-labels images are ...

Blind video quality assessment (BVQA) algorithms are traditionally designed with a two-stage approach - a feature extraction stage that computes typically hand-crafted spatial and/or temporal features, and a regression stage working in the feature space ...

Studying human brain signals has always gathered great attention from the scientific community. In Brain Computer Interface (BCI) research, for example, changes of brain signals in relation to specific tasks (e.g., thinking something) are detected and ...

Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That is, adversarial examples, obtained by adding delicately crafted distortions onto original legal inputs, can mislead a DNN to classify them as any target labels. In a ...

Convolutional Neural Networks (CNNs) have been widely applied to audio classification recently where promising results have been obtained. Previous CNN-based systems mostly learn from two-dimensional time-frequency representations such as MFCC and ...

Facial makeup transfer aims to translate the makeup style from a given reference makeup face image to another non-makeup one while preserving face identity. Such an instance-level transfer problem is more challenging than conventional domain-level ...

Fish recognition is an important task to understand the marine ecosystem and biodiversity. It is often challenging to identify fish species in the wild, due to the following difficulties. First, most fish benchmarks are small-scale, which may limit the ...