Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Crowley, Elliot J."'
Autor:
Ericsson, Linus, Espinosa, Miguel, Yang, Chenhongyi, Antoniou, Antreas, Storkey, Amos, Cohen, Shay B., McDonagh, Steven, Crowley, Elliot J.
Neural architecture search (NAS) finds high performing networks for a given task. Yet the results of NAS are fairly prosaic; they did not e.g. create a shift from convolutional structures to transformers. This is not least because the search spaces i
Externí odkaz:
http://arxiv.org/abs/2405.20838
In continual learning (CL) -- where a learner trains on a stream of data -- standard hyperparameter optimisation (HPO) cannot be applied, as a learner does not have access to all of the data at the same time. This has prompted the development of CL-s
Externí odkaz:
http://arxiv.org/abs/2404.06466
Autor:
Yang, Chenhongyi, Tkach, Anastasia, Hampali, Shreyas, Zhang, Linguang, Crowley, Elliot J., Keskin, Cem
We present EgoPoseFormer, a simple yet effective transformer-based model for stereo egocentric human pose estimation. The main challenge in egocentric pose estimation is overcoming joint invisibility, which is caused by self-occlusion or a limited fi
Externí odkaz:
http://arxiv.org/abs/2403.18080
Autor:
Yang, Chenhongyi, Chen, Zehui, Espinosa, Miguel, Ericsson, Linus, Wang, Zhenyu, Liu, Jiaming, Crowley, Elliot J.
We present PlainMamba: a simple non-hierarchical state space model (SSM) designed for general visual recognition. The recent Mamba model has shown how SSMs can be highly competitive with other architectures on sequential data and initial attempts hav
Externí odkaz:
http://arxiv.org/abs/2403.17695
We present WidthFormer, a novel transformer-based module to compute Bird's-Eye-View (BEV) representations from multi-view cameras for real-time autonomous-driving applications. WidthFormer is computationally efficient, robust and does not require any
Externí odkaz:
http://arxiv.org/abs/2401.03836
Deep Neural Networks (DNNs) are extremely computationally demanding, which presents a large barrier to their deployment on resource-constrained devices. Since such devices are where many emerging deep learning applications lie (e.g., drones, vision-b
Externí odkaz:
http://arxiv.org/abs/2311.08909
Autor:
Espinosa, Miguel, Crowley, Elliot J.
Despite recent advancements in image generation, diffusion models still remain largely underexplored in Earth Observation. In this paper we show that state-of-the-art pretrained diffusion models can be conditioned on cartographic data to generate rea
Externí odkaz:
http://arxiv.org/abs/2308.16648
We present the Group Propagation Vision Transformer (GPViT): a novel nonhierarchical (i.e. non-pyramidal) transformer model designed for general visual recognition with high-resolution features. High-resolution features (or tokens) are a natural fit
Externí odkaz:
http://arxiv.org/abs/2212.06795
Annotating datasets for object detection is an expensive and time-consuming endeavor. To minimize this burden, active learning (AL) techniques are employed to select the most informative samples for annotation within a constrained "annotation budget"
Externí odkaz:
http://arxiv.org/abs/2211.11612
Real-world object detection models should be cheap and accurate. Knowledge distillation (KD) can boost the accuracy of a small, cheap detection model by leveraging useful information from a larger teacher model. However, a key challenge is identifyin
Externí odkaz:
http://arxiv.org/abs/2203.05469