Foundation models pretrained on diverse data at scale have demonstrated extraordinary
capabilities in a wide range of vision and language tasks. When such models are deployed …

Transformers have significantly impacted domains like natural language processing,
computer vision, and robotics, where they improve performance compared to other neural …

Masked autoencoders are scalable vision learners, as the title of MAE\cite {he2022masked},
which suggests that self-supervised learning (SSL) in vision might undertake a similar …

Transformer has been considered the dominating neural architecture in NLP and CV, mostly
under supervised settings. Recently, a similar surge of using Transformers has appeared in …

Designing better deep networks and better reinforcement learning (RL) algorithms are both
important for deep RL. This work focuses on the former. Previous methods build the network …

We demonstrate the capabilities of an attention-based end-to-end approach for high-speed
vision-based quadrotor obstacle avoidance in dense, cluttered environments, with …

The visual world provides an abundance of information, but many input pixels received by
agents often contain distracting stimuli. Autonomous agents need the ability to distinguish …

The Vision Transformer architecture has shown to be competitive in the computer vision
(CV) space where it has dethroned convolution-based networks in several benchmarks …

Transformers are neural network models that utilize multiple layers of self-attention heads
and have exhibited enormous potential in natural language processing tasks. Meanwhile …

Learning from visual observation for efficient robotic manipulation is a hitherto significant
challenge in Reinforcement Learning (RL). Although the collocation of RL policies and …