8,351 research outputs found
Attend and Interact: Higher-Order Object Interactions for Video Understanding
Human actions often involve complex interactions across several inter-related
objects in the scene. However, existing approaches to fine-grained video
understanding or visual relationship detection often rely on single object
representation or pairwise object relationships. Furthermore, learning
interactions across multiple objects in hundreds of frames for video is
computationally infeasible and performance may suffer since a large
combinatorial space has to be modeled. In this paper, we propose to efficiently
learn higher-order interactions between arbitrary subgroups of objects for
fine-grained video understanding. We demonstrate that modeling object
interactions significantly improves accuracy for both action recognition and
video captioning, while saving more than 3-times the computation over
traditional pairwise relationships. The proposed method is validated on two
large-scale datasets: Kinetics and ActivityNet Captions. Our SINet and
SINet-Caption achieve state-of-the-art performances on both datasets even
though the videos are sampled at a maximum of 1 FPS. To the best of our
knowledge, this is the first work modeling object interactions on open domain
large-scale video datasets, and we additionally model higher-order object
interactions which improves the performance with low computational costs.Comment: CVPR 201
Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks
Human action recognition in 3D skeleton sequences has attracted a lot of
research attention. Recently, Long Short-Term Memory (LSTM) networks have shown
promising performance in this task due to their strengths in modeling the
dependencies and dynamics in sequential data. As not all skeletal joints are
informative for action recognition, and the irrelevant joints often bring noise
which can degrade the performance, we need to pay more attention to the
informative ones. However, the original LSTM network does not have explicit
attention ability. In this paper, we propose a new class of LSTM network,
Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action
recognition. This network is capable of selectively focusing on the informative
joints in each frame of each skeleton sequence by using a global context memory
cell. To further improve the attention capability of our network, we also
introduce a recurrent attention mechanism, with which the attention performance
of the network can be enhanced progressively. Moreover, we propose a stepwise
training scheme in order to train our network effectively. Our approach
achieves state-of-the-art performance on five challenging benchmark datasets
for skeleton based action recognition
- …