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Found 14 papers, 2 papers with code
Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition
Nevertheless, how to efficiently model the spatial-temporal skeleton graph without introducing extra computation burden is a challenging problem for industrial deployment.
VS-TransGRU: A Novel Transformer-GRU-based Framework Enhanced by Visual-Semantic Fusion for Egocentric Action Anticipation
Egocentric action anticipation is a challenging task that aims to make advanced predictions of future actions from current and historical observations in the first-person view.
A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation
At present, it is the largest semi-supervised VAD dataset with the largest number of scenes and classes of anomalies, the longest duration, and the only one considering the scene-dependent anomaly.
Co-Occurrence Matters: Learning Action Relation for Temporal Action Localization
Existing works in this field mainly suffer from two weaknesses: (1) They often neglect the multi-label case and only focus on temporal modeling.
Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance
To enhance the discriminative power of features, we propose a batch clustering based loss to encourage a clustering branch to generate distinct normal and abnormal clusters based on a batch of data.
Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection
For the context recovery stream, we propose a spatiotemporal U-Net which can fully utilize the motion information to predict the future frame.
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Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos
For weakly supervised anomaly detection, most existing work is limited to the problem of inadequate video representation due to the inability of modeling long-term contextual information.
Learnable Locality-Sensitive Hashing for Video Anomaly Detection
In this paper, we propose a novel distance-based VAD method to take advantage of all the available normal data efficiently and flexibly.
Time-Domain Doppler Biomotion Detections Immune to Unavoidable DC Offsets
In the past decades, continuous Doppler radar sensor-based bio-signal detections have attracted many research interests.
Efficient Spatialtemporal Context Modeling for Action Recognition
Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition.
Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization
Few-shot learning aims to recognize instances from novel classes with few labeled samples, which has great value in research and application.
Learning to Compare Relation: Semantic Alignment for Few-Shot Learning
First, we introduce a semantic alignment loss to align the relation statistics of the features from samples that belong to the same category.
Egocentric Gesture Recognition Using Recurrent 3D Convolutional Neural Networks With Spatiotemporal Transformer Modules
Gesture is a natural interface in interacting with wearable devices such as VR/AR helmet and glasses.
Body Joint guided 3D Deep Convolutional Descriptors for Action Recognition
To make it end-to-end and do not rely on any sophisticated body joint detection algorithm, we further propose a two-stream bilinear model which can learn the guidance from the body joints and capture the spatio-temporal features simultaneously.
