Adjustable Method Based on Body Parts for Improving the Accuracy of 3D Reconstruction in Visually Important Body Parts from Silhouettes

This research proposes a novel adjustable algorithm for reconstructing 3D body shapes from front and side silhouettes. Most recent silhouette-based approaches use a deep neural network trained by silhouettes and key points to estimate the shape parameters but cannot accurately fit the model to the body contours and consequently are struggling to cover detailed body geometry, especially in the torso. In addition, in most of these cases, body parts have the same accuracy priority, making the optimization harder and avoiding reaching the optimum possible result in essential body parts, like the torso, which is visually important in most applications, such as virtual garment fitting. In the proposed method, we can adjust the expected accuracy for each body part based on our purpose by assigning coefficients for the distance of each body part between the projected 3D body and 2D silhouettes. To measure this distance, we first recognize the correspondent body parts using body segmentation in both views. Then, we align individual body parts by 2D rigid registration and match them using pairwise matching. The objective function tries to minimize the distance cost for the individual body parts in both views based on distances and coefficients by optimizing the statistical model parameters. We also handle the slight variation in the degree of arms and limbs by matching the pose. We evaluate the proposed method with synthetic body meshes from the normalized S-SCAPE. The result shows that the algorithm can more accurately reconstruct visually important body parts with high coefficients.Comment: 16 pages, 17 image

RLAS-BIABC: A Reinforcement Learning-based Answer Selection using the BERT Model Boosted by an Improved ABC Algorithm

Answer selection (AS) is a critical subtask of the open domain question answering (QA) problem. The present paper proposes a method called RLAS-BIABC for AS, which is established on attention mechanism-based Long Short-Term Memory (LSTM) and the Bidirectional Encoder Representations from Transformers (BERT) word embedding, enriched by an improved artificial bee colony algorithm (ABC) algorithm for pre-training and a reinforcement learning-based algorithm for training back-propagation (BP) algorithm. BERT can be comprised in a downstream work and obtain fine-tuned as a united task-specific architecture, and the pre-trained BERT model can grab different linguistic effects. Existing algorithms typically train the AS model with positive-negative pairs for a two-class classifier. A positive pair contains a question and a genuine answer, while a negative one includes a question and a fake answer. The output should be one for positive and zero for negative pairs. Typically, negative pairs are more than positive, leading to an imbalanced classification that drastically reduces system performance. To deal with it, we define classification as a sequential decision-making process in which the agent takes a sample at each step and classifies it. For each classification operation, the agent receives a reward, in which the prize of the majority class is less than the reward of the minority class. Ultimately, the agent finds an optimal value for the policy weights. We initialize the policy weights with the improved ABC algorithm. The initial value technique can prevent problems such as getting stuck in the local optimum. Although ABC serves well in most tasks, there is still a lack in the ABC algorithm that disregards the fitness of related pairs of individuals in discovering a neighboring food source position. Therefore, this paper also proposes a mutual learning technique that modifies the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor. We tested our model on three datasets, LegalQA, TrecQA, and WikiQA, and results show that RLAS-BIABC can be recognized as a state-of-the-art method