Due to the advantage of reducing storage while speeding up query time on big heterogeneous data, cross-modal hashing has been extensively studied for approximate nearest neighbor search of multi-modal data. Most hashing methods assume that training data is class-balanced.However, in practice, real world data often have a long-tailed distribution. In this paper, we introduce a meta-learning based cross-modal hashing method (MetaCMH) to handle long-tailed data. Due to the lack of training samples in the tail classes, MetaCMH first learns direct features from data in different modalities, and then introduces an associative memory module to learn the memory features of samples of the tail classes. It then combines the direct and memory features to obtain meta features for each sample. For samples of the head classes of the long tail distribution, the weight of the direct features is larger, because there are enough training data to learn them well; while for rare classes, the weight of the memory features is larger. Finally, MetaCMH uses a likelihood loss function to preserve the similarity in different modalities and learns hash functions in an end-to-end fashion. Experiments on long-tailed datasets show that MetaCMH performs significantly better than state-of-the-art methods, especially on the tail classes.
Nanyang Technological University（南洋理工大学）；University of California, Berkeley（加州大学伯克利分校）；International Computer Science Institute（国际计算机科学研究所）；The Chinese University of Hong Kong（香港中文大学）；Google Inc.（谷歌公司）
Balanced Meta-Softmax for Long-Tailed Visual Recognition
SenseTime Research（商汤科技研究院）；Nanyang Technological University（南洋理工大学）；National University of Singapore（新加坡国立大学）；The Chinese University of Hong Kong（香港中文大学）
Gaussian meta-embeddings for efficient scoring of a heavy-tailed PLDA model
Nuance Communications（Nuance通信公司）；Brno University of Technology（布尔诺理工大学）；University of Nottingham（诺丁汉大学）；Omilia Conversational Intelligence（Omilia对话智能公司）
A Square Peg in a Square Hole: Meta-Expert for Long-Tailed Semi-Supervised Learning
Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing, China（新一代人工智能技术及其交叉应用重点实验室（东南大学））；School of Computer Science and Engineering, Southeast University, Nanjing, China（计算机科学与工程学院（东南大学））
Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data
Beijing Institute of Technology（北京理工大学）；Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China（光电成像技术与系统教育部重点实验室）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control
University of Maryland（马里兰大学）；Microsoft Research India（微软印度研究院）；National University of Singapore（新加坡国立大学）；Princeton University（普林斯顿大学）；DeepMind（深度思维）；JP Morgan AI Research（摩根大通人工智能研究院）
On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks
Télécom Paris, Institut Polytechnique de Paris（巴黎理工学院巴黎电信学院）；University of Oxford（牛津大学）；Rutgers Business School（罗格斯商学院）；Facebook AI Research（脸书人工智能研究院）
UCB for Large-Scale Pure Exploration: Beyond Sub-Gaussianity
Technology and Operations Management Area, INSEAD, Fontainebleau, France（INSEAD技术与运营管理领域，法国Fontainebleau）；Advanced Institute of Business and School of Economics and Management, Tongji University, Shanghai, China（同济大学商学院高级商学院与经济管理学院，中国上海）；Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota（明尼苏达大学工业与系统工程系，美国明尼苏达州明尼阿波利斯）
