Vocabulary learning that incorporates visual information has become widely recognized as an alternative to context-based methods. However, few studies focus on learners' incorrect answers. On the Other hand, fossilization caused by repeated errors has been a concern. Our proposed system, L-VEIGe, effectively prevents repeated errors by visualizing learners' incorrect answers through image generation, which encourages introspection. However, there exists a 'Feature Disappearance' problem, where the generated images for incorrect answers lack sufficient information for comprehension. This study proposes a method for quantitatively evaluating these error images from a cognitive perspective.

Exceptional advancements in various computer vision tasks, such as identifying and categorizing objects, have been realized through the use of deep learning models, with a particular emphasis on convolutional neural networks (CNNs). Yet, while these models deliver outstanding results, they remain vulnerable to adversarial examples, thereby raising questions about their safety and dependability. In this paper, we investigate the influence of the image characteristics on the efficacy of adversarial patch attack against an image classifier model. We analyzed such characteristics in the frequency domain, where the frequencies indicate the periodicity and information density that contribute to the efficacy of adversarial patches. Our results showed that low-frequency components had significant contribution to the effectiveness of adversarial patch attacks.

This study investigates the cultural dependence of a facial expression recognition (FER) system in an interactive agent by analyzing the performance of several recognition models in different cultural domains. A comprehensive cross-domain classification performance assessment reveals disparities in model performance across different cultural contexts, indicating challenges in cross-cultural FER. To further investigate these characteristics, several public datasets across regions and our cross-cultural dataset of facial expressions derived from Thai and Japanese TV shows are analyzed. By evaluating the capacity of existing FER models to interpret our newly collected data, we found significant variations in emotion interpretation across these cultural contexts, highlighting the necessity for culturally inclusive algorithms. These findings underscore the critical need for more consideration of cultural diversity in FER research, marking a crucial step toward more inclusive and culturally sensitive artificial intelligence technologies.

In recent years, Generative Adversarial Networks (GANs) have gained attention in image synthesis mapping from the latent space onto image space. Trained latent space carries the visual semantics for generated images. Past studies observed that arithmetic operation and linear interpolation in latent space could change the visible facial attributes, such as beards and glasses, in image space. In this work, the visual concepts in the latent space are observed, allowing to change the emotion attribute per facial expressions in the image space. We observed interpolation of a sample while disentangling the emotional attributes to edit the emotion-related facial expressions in the synthesized images. For the experiment, the Deep Convolution Generative Adversarial Networks (DCGANs) are utilized for image synthesis, and Extended Cohn Kanade (CK +) facial expression dataset is applied as the input. Our results showed that manipulating the latent space of the well-trained GANs can edit the emotional aspects of the image space. Moreover, editing facial expressions in the latent space is helpful for the recognition task to improve accuracy. Empirical results showed that the facial expressions classifier improved its performance in the recognition sadness class from 20% to 80% on the imbalance dataset.

Age progression of face images has been an important tool to search for missing children. Many studies on age progression were recently conducted by conditional Generative Adversarial Networks (cGAN) based methods. However, these methods cannot estimate facial aging from a child’s face in the early childhood stage, which exhibits drastic facial shape changes over time. Thus, the problem of age progression from young children remains challenging in the field. In this study, we propose a cGAN based two-stage age progression model considering heritable facial features from parents and child to generate candidates for age-progressed face images from a young child’s face image.

Video prediction is an active machine learning problem to use past information in a video sequence to acquire human-like understanding and then predicting future consequences of object states and actions. The existing prediction frameworks integrated the decomposition and disentanglement techniques to observe object interaction and use them to predict future video scenes. However, the previous works did not consider physical interaction among objects in the prediction. Thus, this research utilizes the physical reasoning concept to represent object dynamics in the real world and estimate future sequences enclosing object dependencies. This paper addresses the investigation of object interaction using stochastic video prediction with physical reasoning representation. We propose a self-supervised framework called Relational Prediction Auto-Encoder (RPAE). Extensive experiments demonstrate that the proposed RPAE can effectively improve the generation and prediction of the near future sequences. We also confirmed the predicted object dynamics by measuring the velocity of each object and its physical interaction in the experiments.

Disasters can have a variety of adverse effects on the terrain and drive people into chaos. Evacuation drills and disaster simulations are standard measures to mitigate this confusion and reduce the cognitive bias that hinders evacuation decisions of the residents. Although various disaster simulation systems are available, localized disaster simulators that can capture the surrounding environment information and synthesize disaster scenes from the victims' perspective are not widely available. Therefore, we propose to simulate a disaster scene from a simple visual image taken from the local area. This study uses conditional Generative Adversarial Network (cGAN) to synthesize flooding images by training generator networks to understand the relationship between original image, semantic segmentation, and corresponding flooding images. By manipulating the segmentation image, it is possible to change the position and type of the objects in the scene to control the generation outputs, allowing users to expand flooding areas or increase flood levels. As a result, the proposed method can generate a consistent flood image containing visual features such as building reflections and waves that look genuine from human perception. Finally, the experiment has shown a promising trend to raise awareness of people in the disaster crisis through flood images generated by our proposed method.

The recent Deep Learning algorithms have demonstrated incredible performance in many fields of research, especially an object detection task where algorithm advances are at a level that can be applied to real-world problems. Classification of plants by leaf image is one of the problems that has been of interest among computer vision researchers for a long time because of its challenges due to a large number of species and their complex features. There are many approaches presented and show promising performance for the task. However, in the tropical area where the diversity of plants is rich, examples of the physical similarity of the leaves in different species can be found easily. Moreover, it is reported that many accidents of misidentifying plants and their usages can be life-threatening. Thus, specimens of plants with similar leaf structures are interested in this research. We present a method to identify plants that are similar in morphological characters of leaf. Using image processing and deep learning techniques and transfer learning of several deep convolutional neural network architectures: VGG-16, ResNet-50, and InceptionV3, the proposed method can identify seven Lamiaceae plants yielded high accuracy prediction of 98.71%, 91.32%, and 98.17%, respectively.

To obtain the 6 DOF extrinsic parameters (rotation and translation matrix) between a 3D ranging sensor and a thermal camera, previous methods require a high-resolution 3D ranging sensor to reliably detect features. Although sparse 3D LiDARs are widely used on autonomous robots, to the best of our knowledge, the extrinsic calibration between a sparse 3D LiDAR (particularly Velodyne VLP-16) and a thermal camera has not been considered in the literature. In this paper, we present a two-step method to address the problem, where a monocular visual camera is used to assist the process. The proposed method decomposes the problem into two steps: extrinsic calibration between a sparse 3D LiDAR and a visual camera; extrinsic calibration between a visual camera and a thermal camera. Experiments are conducted to demonstrate the effectiveness of the proposed two-step method.

So far, most of the facial recognition methods focus on visual image texture and color information. Although they have worked well, most of them still fail to deal with severe illumination changes. In this paper, a novel approach is proposed for facial verification by analyzing thermal data from different organs of the human face. This new thermal facial pattern is free from illumination changes and can even work in a very dark place. In this experiment, facial thermal data were collected from 30 people with diverse genders, ages, and races. Three persons were tracked to verify the consistency of the thermal pattern in normal circumstances, changing light conditions and different physical conditions. A new distance function is introduced for pattern similarity measurement. The proposed approach successfully distinguished different persons with a high verification rate 91.26% according to F-measure and displayed the thermal pattern changes according to different physical conditions.

In this paper, the Gabor filter is studied and further expanded for temporal facial expression analysis. Originally, the Gabor feature describes both spatial and frequency characteristics of 2D images. The prominent of the theorem has been validated in research communities for a decade due to its similarity to the human perception system. The performance of the filter in the existing research gives convincing results on recognizing the human emotions by using a still image. However, the previous research neglects the fact that the understanding of human facial expression of emotions is associated by the dynamic relation, which the motion of expression must be witnessed. Therefore, we propose the novel temporal features by deriving the dynamic of Gabor features in the temporal template representations. Then, we decompose the features onto discriminative subspace for estimating the emotion class.