Hate speech poses a persistent threat to society, causing profound harm to both individuals and communities. Detecting such content is essential for promoting safer and more inclusive environments. While previous research has primarily focused on text-based or image-based hate speech detection, video-based hate detection remains relatively underexplored. A key barrier is the limited availability of high-quality video datasets. Existing hateful video datasets are typically limited in scale, diversity, and annotation depth, often labeling hateful content without further distinguishing between explicit and implicit forms. In this work, we present DeHate, which, to the best of our knowledge, is the largest hateful video dataset to date. DeHate comprises 6689 videos collected from two platforms and spanning six social groups. Each video is annotated with fine-grained labels that differentiate explicit, implicit, and non-hateful content, along with segment-level localization of hate, identification of contributing modalities, and specification of the targeted groups. Through detailed analysis of annotated videos across platforms, we reveal distinct patterns in how hateful content is conveyed, offering a comprehensive comparison between explicit and implicit hate in terms of their prevalence and characteristics. Furthermore, we benchmark state-of-the-art models, including both uni-modal and multi-modal architectures, and identify persistent challenges in detecting subtle and context-dependent forms of hate. Our findings highlight the importance of holistic and fine-grained hateful video datasets for advancing research in hate speech detection. Disclaimer: This paper contains sensitive content that may be disturbing to some readers.

In this paper, we study the problem of one-shot skeleton-based action recognition, which poses unique challenges in learning transferable representation from base classes to novel classes, particularly for fine-grained actions. Existing meta-learning frameworks typically rely on the body-level representations in spatial dimension, which limits the generalisation to capture subtle visual differences in the fine-grained label space. To overcome the above limitation, we propose a part-aware prototypical representation for one-shot skeleton-based action recognition. Our method captures skeleton motion patterns at two distinctive spatial levels, one for global contexts among all body joints, referred to as body level, and the other attends to local spatial regions of body parts, referred to as the part level. We also devise a class-agnostic attention mechanism to highlight important parts for each action class. Specifically, we develop a part-aware prototypical graph network consisting of three modules: a cascaded embedding module for our dual-level modelling, an attention-based part fusion module to fuse parts and generate part-aware prototypes, and a matching module to perform classification with the part-aware representations. We demonstrate the effectiveness of our method on two public skeleton-based action recognition datasets: NTU RGB+D 120 and NW-UCLA.

The task of skeleton-based action recognition remains a core challenge in human-centred scene understanding due to the multiple granularities and large variation in human motion. Existing approaches typically employ a single neural representation for different motion patterns, which has difficulty in capturing fine-grained action classes given limited training data. To address the aforementioned problems, we propose a novel multi-granular spatio-temporal graph network for skeleton-based action classification that jointly models the coarse- and fine-grained skeleton motion patterns. To this end, we develop a dual-head graph network consisting of two interleaved branches, which enables us to extract features at two spatio-temporal resolutions in an effective and efficient manner. Moreover, our network utilises a cross-head communication strategy to mutually enhance the representations of both heads. We conducted extensive experiments on three large-scale datasets, namely NTU RGB+D 60, NTU RGB+D 120, and Kinetics-Skeleton, and achieves the state-of-the-art performance on all the benchmarks, which validates the effectiveness of our method1.