We propose a novel system for active semi-supervised feature-based action recognition. Given time sequences of features tracked during movements our system clusters the sequences into actions. Our system is based on encoder-decoder unsupervised methods shown to perform clustering by self-organization of their latent representation through the auto-regression task. These methods were tested on human action recognition benchmarks and outperformed non-feature based unsupervised methods and achieved comparable accuracy to skeleton-based supervised methods. However, such methods rely on K-Nearest Neighbours (KNN) associating sequences to actions, and general features with no annotated data would correspond to approximate clusters which could be further enhanced. Our system proposes an iterative semi-supervised method to address this challenge and to actively learn the association of clusters and actions. The method utilizes latent space embedding and clustering of the unsupervised encoder-decoder to guide the selection of sequences to be annotated in each iteration. Each iteration, the selection aims to enhance action recognition accuracy while choosing a small number of sequences for annotation. We test the approach on human skeleton-based action recognition benchmarks assuming that only annotations chosen by our method are available and on mouse movements videos recorded in lab experiments. We show that our system can boost recognition performance with only a small percentage of annotations. The system can be used as an interactive annotation tool to guide labeling efforts for 'in the wild' videos of various objects and actions to reach robust recognition.
Iterative Distillation for Better Uncertainty Estimates in Multitask Emotion Recognition
Hong Kong University of Science and Technology（香港科技大学）
当识别情绪时，情绪表现中的细微差别会产生情绪感知的模糊性或不确定性。情感不确定性以前被解释为多个注释者之间的评分者之间的分歧。在本文中，我们考虑了一个更常见和更具挑战性的场景：当只有单个情感标签可用时，对情感不确定性进行建模。从贝叶斯的角度来看，我们建议使用深度集成来捕获多个情感描述符的不确定性，即，动作单元、离散表达式标签和连续描述符。我们进一步应用迭代自蒸馏。多代迭代蒸馏显著提高了情感识别和不确定性估计的性能。我们的方法生成单个学生模型，该模型为域内样本提供准确的不确定性估计，并且生成可以检测域外样本的学生集合。我们使用Aff-wild 2数据集进行的情感识别和不确定性估计的实验表明，我们的算法比Temperature Scaling和Monte Carol Dropout给出了更可靠的不确定性估计。
Iterative Patch Selection for High-Resolution Image Recognition
Hasso Plattner Institute for Digital Health at the Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院哈索·普拉特纳数字健康研究所）；Hasso Plattner Institute for Digital Engineering, University of Potsdam（波茨坦大学哈索·普拉特纳数字工程研究所）；Google Research（谷歌研究院）
One Model for Two Tasks: Cooperatively Recognizing and Recovering Low-Resolution Scene Text Images by Iterative Mutual Guidance
School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；Shanghai Key Lab of Intelligent Information Processing（上海智能信息处理重点实验室）；Tongji University（同济大学）
PIMNet: A Parallel, Iterative and Mimicking Network for Scene Text Recognition
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Communication University of China（中国传媒大学）；Mashang Consumer Finance Co., Ltd.（马上消费金融股份有限公司）
Evaluation of Momentum Diverse Input Iterative Fast Gradient Sign Method (M-DI2-FGSM) Based Attack Method on MCS 2018 Adversarial Attacks on Black Box Face Recognition System
