6 research outputs found
DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting
Understanding human motion behaviour is a critical task for several possible applications like self-driving cars or social robots, and in general for all those settings where an autonomous agent has to navigate inside a human-centric
environment. This is non-trivial because human motion is inherently multi-modal: given a history of human motion paths, there are many plausible ways by which people could move in the future. Additionally, people activities are often driven by goals, e.g. reaching particular locations or interacting with the environment. We address the aforementioned aspects by proposing a new recurrent generative model that considers both single agents' future goals and interactions between different agents. The model exploits a double attention-based graph neural network to collect information about the mutual influences among different agents and to integrate it with data about agents' possible future objectives. Our proposal is general enough to be applied to different scenarios: the model achieves state-of-the-art results in both urban environments and also in sports applications
Generalising via Meta-Examples for Continual Learning in the Wild
Learning quickly and continually is still an ambitious task for neural
networks. Indeed, many real-world applications do not reflect the learning
setting where neural networks shine, as data are usually few, mostly unlabelled
and come as a stream. To narrow this gap, we introduce FUSION - Few-shot
UnSupervIsed cONtinual learning - a novel strategy which aims to deal with
neural networks that "learn in the wild", simulating a real distribution and
flow of unbalanced tasks. We equip FUSION with MEML - Meta-Example
Meta-Learning - a new module that simultaneously alleviates catastrophic
forgetting and favours the generalisation and future learning of new tasks. To
encourage features reuse during the meta-optimisation, our model exploits a
single inner loop per task, taking advantage of an aggregated representation
achieved through the use of a self-attention mechanism. To further enhance the
generalisation capability of MEML, we extend it by adopting a technique that
creates various augmented tasks and optimises over the hardest. Experimental
results on few-shot learning benchmarks show that our model exceeds the other
baselines in both FUSION and fully supervised case. We also explore how it
behaves in standard continual learning consistently outperforming
state-of-the-art approaches.Comment: 16 pages, 11 figures, 13 tables. arXiv admin note: substantial text
overlap with arXiv:2009.0810
AC-VRNN: Attentive Conditional-VRNN for multi-future trajectory prediction
Anticipating human motion in crowded scenarios is essential for developing
intelligent transportation systems, social-aware robots and advanced video
surveillance applications. A key component of this task is represented by the
inherently multi-modal nature of human paths which makes socially acceptable
multiple futures when human interactions are involved. To this end, we propose
a generative architecture for multi-future trajectory predictions based on
Conditional Variational Recurrent Neural Networks (C-VRNNs). Conditioning
mainly relies on prior belief maps, representing most likely moving directions
and forcing the model to consider past observed dynamics in generating future
positions. Human interactions are modeled with a graph-based attention
mechanism enabling an online attentive hidden state refinement of the recurrent
estimation. To corroborate our model, we perform extensive experiments on
publicly-available datasets (e.g., ETH/UCY, Stanford Drone Dataset, STATS
SportVU NBA, Intersection Drone Dataset and TrajNet++) and demonstrate its
effectiveness in crowded scenes compared to several state-of-the-art methods.Comment: Accepted at Computer Vision and Image Understanding (CVIU