Generalized video anomaly event detection: Systematic taxonomy and comparison of deep models
Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance
systems, enabling the temporal or spatial identification of anomalous events within videos …
systems, enabling the temporal or spatial identification of anomalous events within videos …
Disentangled representation learning for multimodal emotion recognition
Multimodal emotion recognition aims to identify human emotions from text, audio, and visual
modalities. Previous methods either explore correlations between different modalities or …
modalities. Previous methods either explore correlations between different modalities or …
Aide: A vision-driven multi-view, multi-modal, multi-tasking dataset for assistive driving perception
Driver distraction has become a significant cause of severe traffic accidents over the past
decade. Despite the growing development of vision-driven driver monitoring systems, the …
decade. Despite the growing development of vision-driven driver monitoring systems, the …
Emotion recognition for multiple context awareness
Understanding emotion in context is a rising hotspot in the computer vision community.
Existing methods lack reliable context semantics to mitigate uncertainty in expressing …
Existing methods lack reliable context semantics to mitigate uncertainty in expressing …
Improving generalization in visual reinforcement learning via conflict-aware gradient agreement augmentation
Learning a policy with great generalization to unseen environments remains challenging but
critical in visual reinforcement learning. Despite the success of augmentation combination in …
critical in visual reinforcement learning. Despite the success of augmentation combination in …
Learning modality-specific and-agnostic representations for asynchronous multimodal language sequences
Understanding human behaviors and intents from videos is a challenging task. Video flows
usually involve time-series data from different modalities, such as natural language, facial …
usually involve time-series data from different modalities, such as natural language, facial …
Deep learning for video anomaly detection: A review
Video anomaly detection (VAD) aims to discover behaviors or events deviating from the
normality in videos. As a long-standing task in the field of computer vision, VAD has …
normality in videos. As a long-standing task in the field of computer vision, VAD has …
DSDCLA: Driving style detection via hybrid CNN-LSTM with multi-level attention fusion
Driving style detection is an essential real-world requirement in diverse contexts, such as
traffic safety, car insurance and fuel consumption optimization. However, the existing …
traffic safety, car insurance and fuel consumption optimization. However, the existing …
Stochastic video normality network for abnormal event detection in surveillance videos
Abstract Video Anomaly Detection (VAD) aims to automatically identify unexpected spatial–
temporal patterns to detect abnormal events in surveillance videos. Existing unsupervised …
temporal patterns to detect abnormal events in surveillance videos. Existing unsupervised …
Learning appearance-motion normality for video anomaly detection
Video anomaly detection is a challenging task in the Computer vision community. Most
single task-based methods do not consider the independence of unique spatial and …
single task-based methods do not consider the independence of unique spatial and …