1,144 research outputs found
Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition
Recently, Long Short-Term Memory (LSTM) has become a popular choice to model
individual dynamics for single-person action recognition due to its ability of
modeling the temporal information in various ranges of dynamic contexts.
However, existing RNN models only focus on capturing the temporal dynamics of
the person-person interactions by naively combining the activity dynamics of
individuals or modeling them as a whole. This neglects the inter-related
dynamics of how person-person interactions change over time. To this end, we
propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to
model the long-term inter-related dynamics between two interacting people on
the bounding boxes covering people. Specifically, for each frame, two
sub-memory units store individual motion information, while a concurrent LSTM
unit selectively integrates and stores inter-related motion information between
interacting people from these two sub-memory units via a new co-memory cell.
Experimental results on the BIT and UT datasets show the superiority of
Co-LSTSM compared with the state-of-the-art methods
Actor-Transformers for Group Activity Recognition
This paper strives to recognize individual actions and group activities from
videos. While existing solutions for this challenging problem explicitly model
spatial and temporal relationships based on location of individual actors, we
propose an actor-transformer model able to learn and selectively extract
information relevant for group activity recognition. We feed the transformer
with rich actor-specific static and dynamic representations expressed by
features from a 2D pose network and 3D CNN, respectively. We empirically study
different ways to combine these representations and show their complementary
benefits. Experiments show what is important to transform and how it should be
transformed. What is more, actor-transformers achieve state-of-the-art results
on two publicly available benchmarks for group activity recognition,
outperforming the previous best published results by a considerable margin.Comment: CVPR 202
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