In this paper, a deep learning-based model for 3D human motion generation from the text is proposed via gesture action classification and an autoregressive model. The model focuses on generating special gestures that express human thinking, such as waving and nodding. To achieve the goal, the proposed method predicts expression from the sentences using a text classification model based on a pretrained language model and generates gestures using the gate recurrent unit-based autoregressive model. Especially, we proposed the loss for the embedding space for restoring raw motions and generating intermediate motions well. Moreover, the novel data augmentation method and stop token are proposed to generate variable length motions. To evaluate the text classification model and 3D human motion generation model, a gesture action classification dataset and action-based gesture dataset are collected. With several experiments, the proposed method successfully generates perceptually natural and realistic 3D human motion from the text. Moreover, we verified the effectiveness of the proposed method using a public-available action recognition dataset to evaluate cross-dataset generalization performance.

This paper introduces a method that efficiently reduces the computational cost and parameter size of Transformer.The proposed model, refer to as Group-Transformer, splits feature space into multiple groups, factorizes the calculation paths, and reduces computations for the group interaction.Extensive experiments on two benchmark tasks, enwik8 and text8, prove our model's effectiveness and efficiency in small-scale Transformers.To the best of our knowledge, Group-Transformer is the first attempt to design Transformer with the group strategy, widely used for efficient CNN architectures.

Many new proposals for scene text recognition (STR) models have been introduced in recent years. While each claim to have pushed the boundary of the technology, a holistic and fair comparison has been largely missing in the field due to the inconsistent choices of training and evaluation datasets. This paper addresses this difficulty with three major contributions. First, we examine the inconsistencies of training and evaluation datasets, and the performance gap results from inconsistencies. Second, we introduce a unified four-stage STR framework that most existing STR models fit into. Using this framework allows for the extensive evaluation of previously proposed STR modules and the discovery of previously unexplored module combinations. Third, we analyze the module-wise contributions to performance in terms of accuracy, speed, and memory demand, under one consistent set of training and evaluation datasets. Such analyses clean up the hindrance on the current comparisons to understand the performance gain of the existing modules. Our code is publicly available.

Rising rate of vandalism against Automatic Teller Machines (ATMs) is a serious issue within banking industries, prompting needs of a technology to autonomously recognize such events. A vision based fusion method proposed here for classifying these incidents is rooted on visually recognizing heavy or sharp objects potentially used for detecting vandalism actions inferred from optical flow. The recognition performance has been improved chiefly by a novel employment of influence functions in selecting data points of each class useful in learning. We show that the tool recognition performance can be improved when the training data is selected from the ImageNet data set as guided by the influence function.

In this paper, a multi-modal classification is proposed for recognizing vandalism against Automatic Teller Machines (ATMs). The visual and textual information base model is developed here to identify external threats on ATMs. The model discriminates threatening behaviors from those that are benign in the image. It provides a level of confidence in the threat recognition by visual object classification coupled with word vector distance measure. To achieve our goal, real-time object detection based on a Region Convolutional Neural Network (R-CNN) first detects objects in the scene and word embedding technique allows to measure distance between the detected object label with predefined tools assumed to be used for vandalizing ATMs. Similarity measure from word embedding not only determines whether the scene may lead to any nefarious activities, but also would provide the level of confidence in occurrence of such incidents. From the experimental evaluation, it is shown that the method is effective and delivers a quantitative measure on decisions it makes.