no code implementations • 25 Apr 2024 • Jaime Spencer, Fabio Tosi, Matteo Poggi, Ripudaman Singh Arora, Chris Russell, Simon Hadfield, Richard Bowden, Guangyuan Zhou, Zhengxin Li, Qiang Rao, Yiping Bao, Xiao Liu, Dohyeong Kim, Jinseong Kim, Myunghyun Kim, Mykola Lavreniuk, Rui Li, Qing Mao, Jiang Wu, Yu Zhu, Jinqiu Sun, Yanning Zhang, Suraj Patni, Aradhye Agarwal, Chetan Arora, Pihai Sun, Kui Jiang, Gang Wu, Jian Liu, Xianming Liu, Junjun Jiang, Xidan Zhang, Jianing Wei, Fangjun Wang, Zhiming Tan, Jiabao Wang, Albert Luginov, Muhammad Shahzad, Seyed Hosseini, Aleksander Trajcevski, James H. Elder
This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC).
1 code implementation • 18 Apr 2024 • Nikolina Kubiak, Armin Mustafa, Graeme Phillipson, Stephen Jolly, Simon Hadfield
In this paper we present S3R-Net, the Self-Supervised Shadow Removal Network.
1 code implementation • 3 Mar 2024 • Jaime Spencer, Chris Russell, Simon Hadfield, Richard Bowden
Self-supervised learning is the key to unlocking generic computer vision systems.
no code implementations • 23 Dec 2023 • Tavis Shore, Simon Hadfield, Oscar Mendez
Cross-view image matching for geo-localisation is a challenging problem due to the significant visual difference between aerial and ground-level viewpoints.
no code implementations • 30 Nov 2023 • Violeta Menéndez González, Andrew Gilbert, Graeme Phillipson, Stephen Jolly, Simon Hadfield
Recent approaches have had great success at performing novel view image synthesis of static scenes.
1 code implementation • ICCV 2023 • Jaime Spencer, Chris Russell, Simon Hadfield, Richard Bowden
Unfortunately, existing approaches limit themselves to the automotive domain, resulting in models incapable of generalizing to complex environments such as natural or indoor settings.
no code implementations • 14 Apr 2023 • Jaime Spencer, C. Stella Qian, Michaela Trescakova, Chris Russell, Simon Hadfield, Erich W. Graf, Wendy J. Adams, Andrew J. Schofield, James Elder, Richard Bowden, Ali Anwar, Hao Chen, Xiaozhi Chen, Kai Cheng, Yuchao Dai, Huynh Thai Hoa, Sadat Hossain, Jianmian Huang, Mohan Jing, Bo Li, Chao Li, Baojun Li, Zhiwen Liu, Stefano Mattoccia, Siegfried Mercelis, Myungwoo Nam, Matteo Poggi, Xiaohua Qi, Jiahui Ren, Yang Tang, Fabio Tosi, Linh Trinh, S. M. Nadim Uddin, Khan Muhammad Umair, Kaixuan Wang, YuFei Wang, Yixing Wang, Mochu Xiang, Guangkai Xu, Wei Yin, Jun Yu, Qi Zhang, Chaoqiang Zhao
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC).
no code implementations • 15 Feb 2023 • Celyn Walters, Simon Hadfield
We present a method to train on event streams derived from standard RL environments, thereby solving the proposed continuous time RL problem.
1 code implementation • 22 Nov 2022 • Jaime Spencer, C. Stella Qian, Chris Russell, Simon Hadfield, Erich Graf, Wendy Adams, Andrew J. Schofield, James Elder, Richard Bowden, Heng Cong, Stefano Mattoccia, Matteo Poggi, Zeeshan Khan Suri, Yang Tang, Fabio Tosi, Hao Wang, Youmin Zhang, Yusheng Zhang, Chaoqiang Zhao
This challenge evaluated the progress of self-supervised monocular depth estimation on the challenging SYNS-Patches dataset.
1 code implementation • 14 Nov 2022 • Violeta Menéndez González, Andrew Gilbert, Graeme Phillipson, Stephen Jolly, Simon Hadfield
This is a view synthesis problem where the number of reference views is limited, and the baseline between target and reference view is significant.
no code implementations • 9 Sep 2022 • Celyn Walters, Simon Hadfield
Despite the success of neural networks in computer vision tasks, digital 'neurons' are a very loose approximation of biological neurons.
2 code implementations • 2 Aug 2022 • Jaime Spencer, Chris Russell, Simon Hadfield, Richard Bowden
It is likely that many papers were not only optimized for particular datasets, but also for errors in the data and evaluation criteria.
no code implementations • 27 Jul 2022 • Yusuf Duman, Jean-yves Guillemaut, Simon Hadfield
A scanning pixel camera is a novel low-cost, low-power sensor that is not diffraction limited.
no code implementations • 16 May 2022 • Xihan Bian, Oscar Mendez, Simon Hadfield
In addition to improving learning efficiency for standard long-term tasks, this approach also makes it possible to perform one-shot generalization to previously unseen tasks, given only a single reference trajectory for the task in a different environment.
no code implementations • 14 May 2022 • Violeta Menéndez González, Andrew Gilbert, Graeme Phillipson, Stephen Jolly, Simon Hadfield
In this work, we present an end-to-end network for stereo-consistent image inpainting with the objective of inpainting large missing regions behind objects.
no code implementations • 6 May 2022 • Bian Xihan, Oscar Mendez, Simon Hadfield
In both cases we out-performed the SOTA by 30% in task success rate.
no code implementations • 12 Apr 2022 • Jaime Spencer, Richard Bowden, Simon Hadfield
We argue that MTL is a stepping stone towards universal feature learning (UFL), which is the ability to learn generic features that can be applied to new tasks without retraining.
1 code implementation • 25 Oct 2021 • Nikolina Kubiak, Armin Mustafa, Graeme Phillipson, Stephen Jolly, Simon Hadfield
We then remap this unified input domain using a discriminator that is presented with the generated outputs and the style reference, i. e. images of the desired illumination conditions.
no code implementations • 22 Sep 2021 • Nikolina Kubiak, Simon Hadfield
We present TACTIC: Task-Aware Compression Through Intelligent Coding.
no code implementations • 1 Sep 2021 • Celyn Walters, Simon Hadfield
The broad scope of obstacle avoidance has led to many kinds of computer vision-based approaches.
no code implementations • 2 Jun 2021 • Xihan Bian, Oscar Mendez, Simon Hadfield
Robots need to be able to work in multiple different environments.
no code implementations • 1 Jun 2021 • Oscar Mendez, Simon Hadfield, Richard Bowden
Recent advances in deep learning hardware allow large likelihood volumes to be stored directly on the GPU, along with the hardware necessary to efficiently perform GPU-bound 3D convolutions and this obviates many of the disadvantages of grid based methods.
2 code implementations • NeurIPS 2020 • Herman Yau, Chris Russell, Simon Hadfield
We present a novel form of explanation for Reinforcement Learning, based around the notion of intended outcome.
no code implementations • 4 Oct 2020 • Peter Blacker, Christopher Paul Bridges, Simon Hadfield
Micro-controller targets are identified where it is only possible to deploy some models if diagonal memory optimisation is used.
no code implementations • 1 Sep 2020 • Necati Cihan Camgoz, Oscar Koller, Simon Hadfield, Richard Bowden
Sign languages use multiple asynchronous information channels (articulators), not just the hands but also the face and body, which computational approaches often ignore.
1 code implementation • CVPR 2020 • Jaime Spencer, Richard Bowden, Simon Hadfield
The aim of this paper is to provide a dense feature representation that can be used to perform localization, sparse matching or image retrieval, regardless of the current seasonal or temporal appearance.
1 code implementation • CVPR 2020 • Jaime Spencer, Richard Bowden, Simon Hadfield
In the current monocular depth research, the dominant approach is to employ unsupervised training on large datasets, driven by warped photometric consistency.
1 code implementation • CVPR 2020 • Necati Cihan Camgoz, Oscar Koller, Simon Hadfield, Richard Bowden
We report state-of-the-art sign language recognition and translation results achieved by our Sign Language Transformers.
1 code implementation • CVPR 2019 • Jaime Spencer, Richard Bowden, Simon Hadfield
In all cases, we show how incorporating SAND features results in better or comparable results to the baseline, whilst requiring little to no additional training.
no code implementations • 19 Nov 2018 • Jaime Spencer, Oscar Mendez, Richard Bowden, Simon Hadfield
In order to build the embedded map, we train a deep Siamese Fully Convolutional U-Net to perform dense feature extraction.
1 code implementation • CVPR 2018 • Necati Cihan Camgoz, Simon Hadfield, Oscar Koller, Hermann Ney, Richard Bowden
SLR seeks to recognize a sequence of continuous signs but neglects the underlying rich grammatical and linguistic structures of sign language that differ from spoken language.
Ranked #10 on Sign Language Translation on RWTH-PHOENIX-Weather 2014 T
2 code implementations • ICCV 2017 • Necati Cihan Camgoz, Simon Hadfield, Oscar Koller, Richard Bowden
We propose a novel deep learning approach to solve simultaneous alignment and recognition problems (referred to as "Sequence-to-sequence" learning).
Ranked #18 on Sign Language Recognition on RWTH-PHOENIX-Weather 2014
no code implementations • ICCV 2017 • Oscar Mendez, Simon Hadfield, Nicolas Pugeault, Richard Bowden
This approach is ill-suited for reconstruction applications, where learning about the environment is more valuable than speed of traversal.
no code implementations • 5 Sep 2017 • Oscar Mendez, Simon Hadfield, Nicolas Pugeault, Richard Bowden
Similarly, we do not extrude the 2D geometry present in the floorplan into 3D and try to align it to the real-world.
no code implementations • ICCV 2015 • Simon Hadfield, Richard Bowden
We present a novel approach to 3D reconstruction which is inspired by the human visual system.
no code implementations • ICCV 2015 • Karel Lebeda, Simon Hadfield, Richard Bowden
We show that the location predictions are robust to camera shake and sud- den motion, which is invaluable for any tracking algorithm and demonstrate this by applying causal prediction to two state-of-the-art trackers.
no code implementations • CVPR 2013 • Simon Hadfield, Richard Bowden
In addition, two state of the art action recognition algorithms are extended to make use of the 3D data, and five new interest point detection strategies are also proposed, that extend to the 3D data.