Video anomaly detection (VAD) has been paid increasing attention due to its
potential applications, its current dominant tasks focus on online detecting
anomalies% at the frame level, which can be roughly interpreted as the binary
or multiple event classification. However, such a setup that builds
relationships between complicated anomalous events and single labels, e.g.,
``vandalism'', is superficial, since single labels are deficient to
characterize anomalous events. In reality, users tend to search a specific
video rather than a series of approximate videos. Therefore, retrieving
anomalous events using detailed descriptions is practical and positive but few
researches focus on this. In this context, we propose a novel task called Video
Anomaly Retrieval (VAR), which aims to pragmatically retrieve relevant
anomalous videos by cross-modalities, e.g., language descriptions and
synchronous audios. Unlike the current video retrieval where videos are assumed
to be temporally well-trimmed with short duration, VAR is devised to retrieve
long untrimmed videos which may be partially relevant to the given query. To
achieve this, we present two large-scale VAR benchmarks, UCFCrime-AR and
XDViolence-AR, constructed on top of prevalent anomaly datasets. Meanwhile, we
design a model called Anomaly-Led Alignment Network (ALAN) for VAR. In ALAN, we
propose an anomaly-led sampling to focus on key segments in long untrimmed
videos. Then, we introduce an efficient pretext task to enhance semantic
associations between video-text fine-grained representations. Besides, we
leverage two complementary alignments to further match cross-modal contents.
Experimental results on two benchmarks reveal the challenges of VAR task and
also demonstrate the advantages of our tailored method.

视频异常检测已引起日益关注，然而目前主要集中于在线帧级别的异常检测任务，而不是利用详细描述来检索异常事件，因此我们提出了一种名为视频异常检索（VAR）的新任务，旨在通过跨模态方法检索相关的异常视频，并设计了一个名为 Anomaly-Led Alignment Network（ALAN）的模型来实现。

从视频异常检测到视频异常检索的进展：新的基准和模型

Towards Video Anomaly Retrieval from Video Anomaly Detection: New  Benchmarks and Model

Multimodal Sentiment Analysis (MSA) has been a popular topic in natural
language processing nowadays, at both sentence and aspect level. However, the
existing approaches almost require large-size labeled datasets, which bring
about large consumption of time and resources. Therefore, it is practical to
explore the method for few-shot sentiment analysis in cross-modalities.
Previous works generally execute on textual modality, using the prompt-based
methods, mainly two types: hand-crafted prompts and learnable prompts. The
existing approach in few-shot multi-modality sentiment analysis task has
utilized both methods, separately. We further design a hybrid pattern that can
combine one or more fixed hand-crafted prompts and learnable prompts and
utilize the attention mechanisms to optimize the prompt encoder. The
experiments on both sentence-level and aspect-level datasets prove that we get
a significant outperformance.

本文提出了一种混合模式的近似零样本多模态情感分析方法，结合手工提示和可学习提示，利用注意力机制优化提示编码器，在句子水平和方面水平数据集上取得了显著的性能优势。

基于句法的混合提示模型用于少样本多模态情感分析

Syntax-aware Hybrid prompt model for Few-shot multi-modal sentiment  analysis

Multi-modal fact verification has become an important but challenging issue
on social media due to the mismatch between the text and images in the
misinformation of news content, which has been addressed by considering
cross-modalities to identify the veracity of the news in recent years. In this
paper, we propose the Pre-CoFactv2 framework with new parameter-efficient
foundation models for modeling fine-grained text and input embeddings with
lightening parameters, multi-modal multi-type fusion for not only capturing
relations for the same and different modalities but also for different types
(i.e., claim and document), and feature representations for explicitly
providing metadata for each sample. In addition, we introduce a unified
ensemble method to boost model performance by adjusting the importance of each
trained model with not only the weights but also the powers. Extensive
experiments show that Pre-CoFactv2 outperforms Pre-CoFact by a large margin and
achieved new state-of-the-art results at the Factify challenge at AAAI 2023. We
further illustrate model variations to verify the relative contributions of
different components. Our team won the first prize (F1-score: 81.82%) and we
made our code publicly available at
this https URL.

本文介绍了 Pre-CoFactv2 框架，使用多模态、多类型融合方法以及特征表示来实现文本与图像的多模态事实验证，并使用统一的集成方法提高模型性能， 最终在 AAAI 2023 的 Factify challenge 中获得了最高成绩和新的最佳结果。