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Detecting the anomaly of human behavior is paramount to timely recognizing endangering situations, such as street fights or elderly falls. However, anomaly detection is complex since anomalous events are rare and because it is an open set recognition task, i.e., what is anomalous at inference has not been observed at training. We propose COSKAD, a novel model that encodes skeletal human motion by a graph convolutional network and learns to COntract SKeletal kinematic embeddings onto a latent hypersphere of minimum volume for Video Anomaly Detection. We propose three latent spaces: the commonly-adopted Euclidean and the novel spherical and hyperbolic. All variants outperform the state-of-the-art on the most recent UBnormal dataset, for which we contribute a human-related version with annotated skeletons. COSKAD sets a new state-of-the-art on the human-related versions of ShanghaiTech Campus and CUHK Avenue , with performance comparable to video-based methods. Source code and dataset will be released upon acceptance. • We propose COSKAD: COntracting SKeletal kinematic embedding for HR video Anomaly Detection. • COSKAD explores three metric spaces where the latent distribution is altered. • State-of-the-art performance on three benchmarks: HR-UBnormal, HR-Avenue, and HR-STC. • We introduce a new skeleton-based Human-Related benchmark, HR-UBnormal. • The paper presents an in-depth analysis of the components and manifolds of COSKAD. [ABSTRACT FROM AUTHOR]

• First in-depth experimental study of Transformer Networks and BERT for trajectory forecasting. • Comparative evaluation on the modelling of individual human trajectories. • First exhaustive evaluation of input/output representations and problem formulations. • Focus on the capability of Transformers to predict multi-modal futures. • Analysis of the impact of intention and long-term forecasting. Transformer Networks have established themselves as the de-facto state-of-the-art for trajectory forecasting but there is currently no systematic study on their capability to model the motion patterns of people, without interactions with other individuals nor the social context. There is abundant literature on LSTMs, CNNs and GANs on this subject. However methods adopting Transformer techniques achieve great performances by complex models and a clear analysis of their adoption as plain sequence models is missing. This paper proposes the first in-depth study of Transformer Networks (TF) and the Bidirectional Transformers (BERT) for the forecasting of the individual motion of people, without bells and whistles. We conduct an exhaustive evaluation of the input/output representations, problem formulations and sequence modelling, including a novel analysis of their capability to predict multi-modal futures. Out of comparative evaluation on the ETH+UCY benchmark, both TF and BERT are top performers in predicting individual motions and remain within a narrow margin wrt more complex techniques, including both social interactions and scene contexts. Source code will be released for all conducted experiments. [ABSTRACT FROM AUTHOR]

• Prior works treat few-shot fine-grained classification and person search separately. • The proposed query-guided networks (QGN) address both tasks in a unified framework. • QGN introduces query-guidance for the task of few-shot fine-grained recognition. • State-of-the-art performance on CUB, FGVC-Aircraft, Stanford Dogs datasets. • The paper performs an in-depth analysis of each of the query-guided components of QGN. [Display omitted] Few-shot fine-grained classification and person search appear as distinct tasks and literature has treated them separately. But a closer look unveils important similarities: both tasks target categories that can only be discriminated by specific object details; and the relevant models should generalize to new categories, not seen during training. We propose a novel unified Query-Guided Network (QGN) applicable to both tasks. QGN consists of a Query-guided Siamese-Squeeze-and-Excitation subnetwork which re-weights both the query and gallery features across all network layers, a Query-guided Region Proposal subnetwork for query-specific localisation, and a Query-guided Similarity subnetwork for metric learning. QGN improves on a few recent few-shot fine-grained datasets, outperforming other techniques on CUB by a large margin. QGN also performs competitively on the person search CUHK-SYSU and PRW datasets, where we perform in-depth analysis. [ABSTRACT FROM AUTHOR]