Transductive Multi-View Zero-Shot Learning

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Learning Multimodal Latent Attributes

Abstract—The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning

Study of quasi-distributed optical fiber methane sensors based on laser absorption spectrometry

The coal industry plays an important role in the economic development of China. With the increase of coal mining year by year, coal mine accidents caused by gas explosion also occur frequently, which poses a serious threat to the life safety of absenteeism and national property safety. Therefore, high-precision methane fiber sensor is of great significance to ensure coal mine safety. This paper mainly introduces two kinds of quasi-distributed gas optical fiber sensing systems based on laser absorption spectroscopy. The gas fiber optic sensor based on absorption spectrum has high measurement accuracy, fast response and long service life. One is quasi-distributed optical fiber sensing system based on spatial division multiplexing (SDM) technology and the other is quasi-distributed optical fiber sensing system based on optical time domain reflection and time division multiplexing(TDM) technology

Multidisciplinary approaches in evolutionary linguistics

Studying language evolution has become resurgent in modern scientific research. In this revival field, approaches from a number of disciplines other than linguistics, including (paleo)anthropology and archaeology, animal behaviors, genetics, neuroscience, computer simulation, and psychological experimentation, have been adopted, and a wide scope of topics have been examined in one way or another, covering not only world languages, but also human behaviors, brains and cultural products, as well as nonhuman primates and other species remote to humans. In this paper, together with a survey of recent findings based on these many approaches, we evaluate how this multidisciplinary perspective yields important insights into a comprehensive understanding of language, its evolution, and human cognition.postprin

What Does CNN Shift Invariance Look Like? A Visualization Study

Feature extraction with convolutional neural networks (CNNs) is a popular method to represent images for machine learning tasks. These representations seek to capture global image content, and ideally should be independent of geometric transformations. We focus on measuring and visualizing the shift invariance of extracted features from popular off-the-shelf CNN models. We present the results of three experiments comparing representations of millions of images with exhaustively shifted objects, examining both local invariance (within a few pixels) and global invariance (across the image frame). We conclude that features extracted from popular networks are not globally invariant, and that biases and artifacts exist within this variance. Additionally, we determine that anti-aliased models significantly improve local invariance but do not impact global invariance. Finally, we provide a code repository for experiment reproduction, as well as a website to interact with our results at https://jakehlee.github.io/visualize-invariance.Comment: Presented at the 2020 ECCV Workshop on Real-World Computer Vision from Inputs with Limited Quality (RLQ-TOD 2020), Glasgow, Scotlan

Write Pattern Format Algorithm for Reliable NAND-Based SSDs

This brief presents and evaluates a pre-coding algorithm to reduce power consumption and improve data retention in NAND-based solid-state drives. Compared to the state-of-the-art asymmetric coding and stripe pattern elimination algorithm, the proposed write pattern format algorithm (WPFA) achieves better data retention while consuming less power. The hardware for WPFA is simpler and requires less circuitry. The performance of WPFA is evaluated by both computer simulations and field-programmable gate array implementation