89 research outputs found
Virtual to Real Reinforcement Learning for Autonomous Driving
Reinforcement learning is considered as a promising direction for driving
policy learning. However, training autonomous driving vehicle with
reinforcement learning in real environment involves non-affordable
trial-and-error. It is more desirable to first train in a virtual environment
and then transfer to the real environment. In this paper, we propose a novel
realistic translation network to make model trained in virtual environment be
workable in real world. The proposed network can convert non-realistic virtual
image input into a realistic one with similar scene structure. Given realistic
frames as input, driving policy trained by reinforcement learning can nicely
adapt to real world driving. Experiments show that our proposed virtual to real
(VR) reinforcement learning (RL) works pretty well. To our knowledge, this is
the first successful case of driving policy trained by reinforcement learning
that can adapt to real world driving data
Learning to Anticipate Future with Dynamic Context Removal
Anticipating future events is an essential feature for intelligent systems
and embodied AI. However, compared to the traditional recognition task, the
uncertainty of future and reasoning ability requirement make the anticipation
task very challenging and far beyond solved. In this filed, previous methods
usually care more about the model architecture design or but few attention has
been put on how to train an anticipation model with a proper learning policy.
To this end, in this work, we propose a novel training scheme called Dynamic
Context Removal (DCR), which dynamically schedules the visibility of observed
future in the learning procedure. It follows the human-like curriculum learning
process, i.e., gradually removing the event context to increase the
anticipation difficulty till satisfying the final anticipation target. Our
learning scheme is plug-and-play and easy to integrate any reasoning model
including transformer and LSTM, with advantages in both effectiveness and
efficiency. In extensive experiments, the proposed method achieves
state-of-the-art on four widely-used benchmarks. Our code and models are
publicly released at https://github.com/AllenXuuu/DCR.Comment: CVPR 202
Constructing Balance from Imbalance for Long-tailed Image Recognition
Long-tailed image recognition presents massive challenges to deep learning
systems since the imbalance between majority (head) classes and minority (tail)
classes severely skews the data-driven deep neural networks. Previous methods
tackle with data imbalance from the viewpoints of data distribution, feature
space, and model design, etc.In this work, instead of directly learning a
recognition model, we suggest confronting the bottleneck of head-to-tail bias
before classifier learning, from the previously omitted perspective of
balancing label space. To alleviate the head-to-tail bias, we propose a concise
paradigm by progressively adjusting label space and dividing the head classes
and tail classes, dynamically constructing balance from imbalance to facilitate
the classification. With flexible data filtering and label space mapping, we
can easily embed our approach to most classification models, especially the
decoupled training methods. Besides, we find the separability of head-tail
classes varies among different features with different inductive biases. Hence,
our proposed model also provides a feature evaluation method and paves the way
for long-tailed feature learning. Extensive experiments show that our method
can boost the performance of state-of-the-arts of different types on
widely-used benchmarks. Code is available at https://github.com/silicx/DLSA.Comment: Accepted to ECCV 202
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