Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Hu, Anthony"'
Text-to-image models have shown remarkable progress in generating high-quality images from user-provided prompts. Despite this, the quality of these images varies due to the models' sensitivity to human language nuances. With advancements in large la
Externí odkaz:
http://arxiv.org/abs/2406.08713
Autor:
Ishida, Shu, Corrado, Gianluca, Fedoseev, George, Yeo, Hudson, Russell, Lloyd, Shotton, Jamie, Henriques, João F., Hu, Anthony
We propose LangProp, a framework for iteratively optimizing code generated by large language models (LLMs), in both supervised and reinforcement learning settings. While LLMs can generate sensible coding solutions zero-shot, they are often sub-optima
Externí odkaz:
http://arxiv.org/abs/2401.10314
Autor:
Hu, Anthony, Russell, Lloyd, Yeo, Hudson, Murez, Zak, Fedoseev, George, Kendall, Alex, Shotton, Jamie, Corrado, Gianluca
Autonomous driving promises transformative improvements to transportation, but building systems capable of safely navigating the unstructured complexity of real-world scenarios remains challenging. A critical problem lies in effectively predicting th
Externí odkaz:
http://arxiv.org/abs/2309.17080
Autor:
Hu, Anthony
Humans navigate in their environment by learning a mental model of the world through passive observation and active interaction. Their world model allows them to anticipate what might happen next and act accordingly with respect to an underlying obje
Externí odkaz:
http://arxiv.org/abs/2306.09179
Autor:
Hu, Anthony, Corrado, Gianluca, Griffiths, Nicolas, Murez, Zak, Gurau, Corina, Yeo, Hudson, Kendall, Alex, Cipolla, Roberto, Shotton, Jamie
An accurate model of the environment and the dynamic agents acting in it offers great potential for improving motion planning. We present MILE: a Model-based Imitation LEarning approach to jointly learn a model of the world and a policy for autonomou
Externí odkaz:
http://arxiv.org/abs/2210.07729
Translational invariance induced by pooling operations is an inherent property of convolutional neural networks, which facilitates numerous computer vision tasks such as classification. Yet to leverage rotational invariant tasks, convolutional archit
Externí odkaz:
http://arxiv.org/abs/2111.14507
Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of solar irradiance resulting from the cloud cover dynamics is based on
Externí odkaz:
http://arxiv.org/abs/2104.12419
Autor:
Hu, Anthony, Murez, Zak, Mohan, Nikhil, Dudas, Sofía, Hawke, Jeffrey, Badrinarayanan, Vijay, Cipolla, Roberto, Kendall, Alex
Driving requires interacting with road agents and predicting their future behaviour in order to navigate safely. We present FIERY: a probabilistic future prediction model in bird's-eye view from monocular cameras. Our model predicts future instance s
Externí odkaz:
http://arxiv.org/abs/2104.10490
We present a novel deep learning architecture for probabilistic future prediction from video. We predict the future semantics, geometry and motion of complex real-world urban scenes and use this representation to control an autonomous vehicle. This w
Externí odkaz:
http://arxiv.org/abs/2003.06409
We present a novel embedding approach for video instance segmentation. Our method learns a spatio-temporal embedding integrating cues from appearance, motion, and geometry; a 3D causal convolutional network models motion, and a monocular self-supervi
Externí odkaz:
http://arxiv.org/abs/1912.08969