4 accepted papers
While existing Learning from intervention (LfI) methods within the human-in-the-loop reinforcement learning (HiL-RL) paradigm mainly operate on the assumption that human policies are homogeneous and deterministic with low variance, natural human driving behaviors are multimodal with intrinsic uncert…
"Early exiting is an effective paradigm for improving the inference efficiency of deep networks. By constructing classifiers with varying resource demands (the exits), such networks allow easy samples to be output at early exits, removing the need for executing deeper layers. While existing works ma…
Visual tracking is a fundamental capability for robots tasked with humans and environment interaction. However, state-of-the-art visual tracking methods are still prone to failures and are imprecise when applied to challenging stereos, and their results are generally confidence agonistic. These meth…
Object tracking is an important capability for robots tasked with interacting with humans and the environment, and it enables robots to manipulate objects. In object tracking, selecting samples to learn a robust and efficient appearance model is a challenging task. Model learning determines both the…