Weakly supervised video anomaly detection (WSVAD) presents a challenging task focused on detecting frame-level anomalies using only video-level labels. However, existing methods focus mainly on visual modalities, neglecting rich multi-modality information. This paper proposes a novel framework, Cross-Modality Heterogeneous Knowledge Fusion (CMHKF), that integrates cross-modality knowledge from video, audio, and text to improve anomaly detection and localization. To achieve adaptive cross-modality heterogeneous knowledge learning, we designed two components: Cross-Modality Video-Text Knowledge Alignment (CVKA) and Audio Modality Feature Adaptive Extraction (AFAE). They extract and aggregate features by exploring inter-modality correlations. By leveraging abundant cross-modality knowledge, our approach improves the discrimination between normal and anomalous segments. Extensive experiments on XD-Violence show our method significantly enhances accuracy and robustness in both coarse-grained and fine-grained anomaly detection.

Yongsen Zheng, Ruilin Xu, Ziliang Chen, Guohua Wang, Mingjie Qian, Jinghui Qin, Liang Lin. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

The Matthew effect is a big challenge in Recommender Systems (RSs), where popular items tend to receive increasing attention, while less popular ones are often overlooked, perpetuating existing disparities.Although many existing methods attempt to mitigate Matthew effect in the static or quasi-static recommendation scenarios, such issue will be more pronounced as users engage with the system over time.To this end, we propose a novel framework, Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation (HiCore), aiming to address Matthew effect in the Conversational Recommender System (CRS) involving the dynamic user-system feedback loop.It devotes to learn multi-level user interests by building a set of hypergraphs (i.e., item-, entity-, word-oriented multiple-channel hypergraphs) to alleviate the Matthew effec.Extensive experiments on four CRS-based datasets showcase that HiCore attains a new state-of-the-art performance, underscoring its superiority in mitigating the Matthew effect effectively.Our code is available at https://github.com/zysensmile/HiCore.

Diversity plays a crucial role in Recommender Systems (RSs) as it ensures a wide range of recommended items, providing users with access to new and varied options. Without diversity, users often encounter repetitive content, limiting their exposure to novel choices. While significant efforts begin to enhance recommendation diversification in static offline scenarios, relatively less attention has been given to online Conversational Recommender Systems (CRSs). However, the lack of recommendation diversity in CRSs will increasingly exacerbate over time due to the dynamic user-system feedback loop, resulting in challenges such as the Matthew effect, filter bubbles, and echo chambers. To address these issues, we propose a novel paradigm, User-Centric Multi-Interest Learning for Conversational Movie Recommendation (CoMoRec), aiming to learn multiple user interests to improve result diversity for movie recommendations. Firstly, CoMoRec automatically models various facets of user interests, including context-, graph-, and review-based interests, to explore a wide range of user potential intentions. Then, it leverages these multi-aspect user interests to accurately predict personalized and diverse movie recommendations and generate fluent and informative responses during conversations. Extensive experiments on two publicly CRS-based movie datasets show that our CoMoRec achieves a new state-of-the-art performance and the superiority of improving recommendation diversity in the CRS.