In the film industry, movie posters have been an essential part of
advertising and marketing for many decades, and continue to play a vital role
even today in the form of digital posters through online, social media and OTT
platforms. Typically, movie posters can effectively promote and communicate the
essence of a film, such as its genre, visual style/ tone, vibe and storyline
cue/ theme, which are essential to attract potential viewers. Identifying the
genres of a movie often has significant practical applications in recommending
the film to target audiences. Previous studies on movie genre identification
are limited to subtitles, plot synopses, and movie scenes that are mostly
accessible after the movie release. Posters usually contain pre-release
implicit information to generate mass interest. In this paper, we work for
automated multi-label genre identification only from movie poster images,
without any aid of additional textual/meta-data information about movies, which
is one of the earliest attempts of its kind. Here, we present a deep
transformer network with a probabilistic module to identify the movie genres
exclusively from the poster. For experimental analysis, we procured 13882
number of posters of 13 genres from the Internet Movie Database (IMDb), where
our model performances were encouraging and even outperformed some major
contemporary architectures.

本文介绍了一种基于深度转换网络和概率模型的电影海报图像自动多标签类型识别方法，不需要任何额外的文本或元数据信息，通过实验分析在 13882 个电影海报中的 13 个类型中表现出色并超过了一些主要的现代架构。

电影海报多标签类型识别中的视觉特征揭秘

Demystifying Visual Features of Movie Posters for Multi-Label Genre  Identification

Efficient anomaly detection and diagnosis in multivariate time-series data is
of great importance for modern industrial applications. However, building a
system that is able to quickly and accurately pinpoint anomalous observations
is a challenging problem. This is due to the lack of anomaly labels, high data
volatility and the demands of ultra-low inference times in modern applications.
Despite the recent developments of deep learning approaches for anomaly
detection, only a few of them can address all of these challenges. In this
paper, we propose TranAD, a deep transformer network based anomaly detection
and diagnosis model which uses attention-based sequence encoders to swiftly
perform inference with the knowledge of the broader temporal trends in the
data. TranAD uses focus score-based self-conditioning to enable robust
multi-modal feature extraction and adversarial training to gain stability.
Additionally, model-agnostic meta learning (MAML) allows us to train the model
using limited data. Extensive empirical studies on six publicly available
datasets demonstrate that TranAD can outperform state-of-the-art baseline
methods in detection and diagnosis performance with data and time-efficient
training. Specifically, TranAD increases F1 scores by up to 17%, reducing
training times by up to 99% compared to the baselines.

本文提出了基于深度变换网络的异常检测和诊断模型 ——TranAD。该模型采用基于注意力的序列编码器进行推断，能够快速、准确地确定异常观测值，并采用聚焦得分自制约和对抗训练等方法提高特征提取和模型稳定性。实验结果表明，TranAD 在数据和时间效率上优于现有的基准方法。