18 research outputs found
Meta Adaptation using Importance Weighted Demonstrations
Imitation learning has gained immense popularity because of its high
sample-efficiency. However, in real-world scenarios, where the trajectory
distribution of most of the tasks dynamically shifts, model fitting on
continuously aggregated data alone would be futile. In some cases, the
distribution shifts, so much, that it is difficult for an agent to infer the
new task. We propose a novel algorithm to generalize on any related task by
leveraging prior knowledge on a set of specific tasks, which involves assigning
importance weights to each past demonstration. We show experiments where the
robot is trained from a diversity of environmental tasks and is also able to
adapt to an unseen environment, using few-shot learning. We also developed a
prototype robot system to test our approach on the task of visual navigation,
and experimental results obtained were able to confirm these suppositions
Detecting events and key actors in multi-person videos
Multi-person event recognition is a challenging task, often with many people
active in the scene but only a small subset contributing to an actual event. In
this paper, we propose a model which learns to detect events in such videos
while automatically "attending" to the people responsible for the event. Our
model does not use explicit annotations regarding who or where those people are
during training and testing. In particular, we track people in videos and use a
recurrent neural network (RNN) to represent the track features. We learn
time-varying attention weights to combine these features at each time-instant.
The attended features are then processed using another RNN for event
detection/classification. Since most video datasets with multiple people are
restricted to a small number of videos, we also collected a new basketball
dataset comprising 257 basketball games with 14K event annotations
corresponding to 11 event classes. Our model outperforms state-of-the-art
methods for both event classification and detection on this new dataset.
Additionally, we show that the attention mechanism is able to consistently
localize the relevant players.Comment: Accepted for publication in CVPR'1
SCE: Scalable Network Embedding from Sparsest Cut
Large-scale network embedding is to learn a latent representation for each
node in an unsupervised manner, which captures inherent properties and
structural information of the underlying graph. In this field, many popular
approaches are influenced by the skip-gram model from natural language
processing. Most of them use a contrastive objective to train an encoder which
forces the embeddings of similar pairs to be close and embeddings of negative
samples to be far. A key of success to such contrastive learning methods is how
to draw positive and negative samples. While negative samples that are
generated by straightforward random sampling are often satisfying, methods for
drawing positive examples remains a hot topic.
In this paper, we propose SCE for unsupervised network embedding only using
negative samples for training. Our method is based on a new contrastive
objective inspired by the well-known sparsest cut problem. To solve the
underlying optimization problem, we introduce a Laplacian smoothing trick,
which uses graph convolutional operators as low-pass filters for smoothing node
representations. The resulting model consists of a GCN-type structure as the
encoder and a simple loss function. Notably, our model does not use positive
samples but only negative samples for training, which not only makes the
implementation and tuning much easier, but also reduces the training time
significantly.
Finally, extensive experimental studies on real world data sets are
conducted. The results clearly demonstrate the advantages of our new model in
both accuracy and scalability compared to strong baselines such as GraphSAGE,
G2G and DGI.Comment: KDD 202