Directional Temporal Modeling for Action Recognition
Many current activity recognition models use 3D convolutional neural networks (e.g. I3D, I3D-NL) to generate local spatial-temporal features. However, such features do not encode clip-level ordered temporal information. In this paper, we introduce a channel independent directional convolution (CIDC) operation, which learns to model the temporal evolution among local features. By applying multiple CIDC units we construct a light-weight network that models the clip-level temporal evolution across multiple spatial scales. Our CIDC network can be attached to any activity recognition backbone network. We evaluate our method on four popular activity recognition datasets and consistently improve upon state-of-the-art techniques. We further visualize the activation map of our CIDC network and show that it is able to focus on more meaningful, action related parts of the frame.
Code (0)
등록된 구현이 없습니다.
Tasks
Action RecognitionActivity RecognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Late Temporal Modeling in 3D CNN Architectures with BERT for Action Recognition
In this work, we combine 3D convolution with late temporal modeling for action recognition. For this aim, we replace the conventional Temporal Global Average Pooling (TGAP) layer at the end of 3D convolutional architectu…
Action RecognitionBidirectional Temporal Dynamics Modeling for EEG-based Driving Fatigue Recognition
Driving fatigue is a major contributor to traffic accidents and poses a serious threat to road safety. Electroencephalography (EEG) provides a direct measurement of neural activity, yet EEG-based fatigue recognition is h…
Shrinkage Optimized Directed Information using Pictorial Structures for Action Recognition
In this paper, we propose a novel action recognition framework. The method uses pictorial structures and shrinkage optimized directed information assessment (SODA) coupled with Markov Random Fields called SODA+MRF to mod…
Action RecognitionTemporal Action LocalizationAn Attention-Enhanced Recurrent Graph Convolutional Network for Skeleton-Based Action Recognition
Dynamic movements of human skeleton have attracted more and more attention as a robust modality for action recognition. As not all temporal stages and skeleton joints are informative for action recognition, and the irrel…
Action RecognitionSkeleton Based Action RecognitionHuman Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework
Vision-based human activity recognition has emerged as one of the essential research areas in video analytics domain. Over the last decade, numerous advanced deep learning algorithms have been introduced to recognize com…
Action RecognitionActivity RecognitionComputational EfficiencyHuman Activity Recognition