1 code implementation • ECCV 2020 • Zhenbo Song, Wayne Chen, Dylan Campbell, Hongdong Li
We propose a new deep neural network which takes a colored 3D point cloud of a scene, and directly synthesizes a photo-realistic image from an arbitrary viewpoint.
no code implementations • 1 Apr 2024 • Joao F. Henriques, Dylan Campbell, Tengda Han
As the horses have long left the barn, our proposal may be seen as antiquated and irrelevant.
no code implementations • 12 Feb 2024 • Weijie Tu, Weijian Deng, Dylan Campbell, Stephen Gould, Tom Gedeon
Vision--Language Models (VLMs) have emerged as the dominant approach for zero-shot recognition, adept at handling diverse scenarios and significant distribution changes.
no code implementations • 19 Jan 2024 • Dominik A. Kloepfer, João F. Henriques, Dylan Campbell
We relax this assumption by removing the requirement of 3D structure, e. g., depth maps or point clouds, and only require camera pose information, which can be obtained from odometry.
no code implementations • 7 Dec 2023 • Yiqun Zhang, Zhenyue Qin, Yang Liu, Dylan Campbell
We introduce a pipeline to address anatomical inaccuracies in Stable Diffusion generated hand images.
1 code implementation • 12 Nov 2023 • Zhaoyuan Yang, Zhengyang Yu, Zhiwei Xu, Jaskirat Singh, Jing Zhang, Dylan Campbell, Peter Tu, Richard Hartley
We present a diffusion-based image morphing approach with perceptually-uniform sampling (IMPUS) that produces smooth, direct and realistic interpolations given an image pair.
no code implementations • ICCV 2023 • Dominik A. Kloepfer, Dylan Campbell, João F. Henriques
We start from the idea that the training objective can be framed as a patch retrieval problem: given an image patch in one view of a scene, we would like to retrieve (with high precision and recall) all patches in other views that map to the same real-world location.
1 code implementation • ICCV 2023 • Frederic Z. Zhang, Yuhui Yuan, Dylan Campbell, Zhuoyao Zhong, Stephen Gould
Recently, the DETR framework has emerged as the dominant approach for human--object interaction (HOI) research.
Ranked #2 on Human-Object Interaction Detection on HICO-DET
no code implementations • 6 Jul 2023 • Peter Tu, Zhaoyuan Yang, Richard Hartley, Zhiwei Xu, Jing Zhang, Yiwei Fu, Dylan Campbell, Jaskirat Singh, Tianyu Wang
This paper begins with a description of methods for estimating image probability density functions that reflects the observation that such data is usually constrained to lie in restricted regions of the high-dimensional image space-not every pattern of pixels is an image.
1 code implementation • 13 Jun 2023 • Ge-Peng Ji, Jing Zhang, Dylan Campbell, Huan Xiong, Nick Barnes
Unlike existing fully-supervised approaches, we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but effective self-supervised learning approach.
no code implementations • 13 Jun 2022 • Eldar Insafutdinov, Dylan Campbell, João F. Henriques, Andrea Vedaldi
We present a method for the accurate 3D reconstruction of partly-symmetric objects.
no code implementations • 31 Mar 2022 • Samuel Albanie, Dylan Campbell, João F. Henriques
The field of machine learning has achieved striking progress in recent years, witnessing breakthrough results on language modelling, protein folding and nitpickingly fine-grained dog breed classification.
1 code implementation • 26 Mar 2022 • Yujiao Shi, Xin Yu, Liu Liu, Dylan Campbell, Piotr Koniusz, Hongdong Li
We address the problem of ground-to-satellite image geo-localization, that is, estimating the camera latitude, longitude and orientation (azimuth angle) by matching a query image captured at the ground level against a large-scale database with geotagged satellite images.
no code implementations • 24 Feb 2022 • Stephen Gould, Dylan Campbell, Itzik Ben-Shabat, Chamin Hewa Koneputugodage, Zhiwei Xu
Deep declarative networks and other recent related works have shown how to differentiate the solution map of a (continuous) parametrized optimization problem, opening up the possibility of embedding mathematical optimization problems into end-to-end learnable models.
1 code implementation • CVPR 2022 • Frederic Z. Zhang, Dylan Campbell, Stephen Gould
Recent developments in transformer models for visual data have led to significant improvements in recognition and detection tasks.
Ranked #10 on Human-Object Interaction Detection on V-COCO
2 code implementations • NeurIPS 2021 • Mandela Patrick, Dylan Campbell, Yuki M. Asano, Ishan Misra, Florian Metze, Christoph Feichtenhofer, Andrea Vedaldi, João F. Henriques
In video transformers, the time dimension is often treated in the same way as the two spatial dimensions.
Ranked #15 on Action Recognition on EPIC-KITCHENS-100 (using extra training data)
1 code implementation • 11 Apr 2021 • Ali Cheraghian, Shafinn Rahman, Townim F. Chowdhury, Dylan Campbell, Lars Petersson
Zero-shot learning, the task of learning to recognize new classes not seen during training, has received considerable attention in the case of 2D image classification.
2 code implementations • ICCV 2021 • Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, Richard Hartley
We demonstrate that the optical flow estimates in the occluded regions can be significantly improved without damaging the performance in non-occluded regions.
Ranked #5 on Optical Flow Estimation on Sintel-final
1 code implementation • 2 Mar 2021 • Yujiao Shi, Dylan Campbell, Xin Yu, Hongdong Li
Specifically, we observe that when a 3D point in the real world is visible in both views, there is a deterministic mapping between the projected points in the two-view images given the height information of this 3D point.
2 code implementations • ICCV 2021 • Frederic Z. Zhang, Dylan Campbell, Stephen Gould
We address the problem of detecting human-object interactions in images using graphical neural networks.
Ranked #18 on Human-Object Interaction Detection on V-COCO (using extra training data)
2 code implementations • ECCV 2020 • Dylan Campbell, Liu Liu, Stephen Gould
We instead propose the first fully end-to-end trainable network for solving the blind PnP problem efficiently and globally, that is, without the need for pose priors.
1 code implementation • 1 Jul 2020 • Yizhak Ben-Shabat, Xin Yu, Fatemeh Sadat Saleh, Dylan Campbell, Cristian Rodriguez-Opazo, Hongdong Li, Stephen Gould
The availability of a large labeled dataset is a key requirement for applying deep learning methods to solve various computer vision tasks.
1 code implementation • CVPR 2020 • Yujiao Shi, Xin Yu, Dylan Campbell, Hongdong Li
Cross-view geo-localization is the problem of estimating the position and orientation (latitude, longitude and azimuth angle) of a camera at ground level given a large-scale database of geo-tagged aerial (e. g., satellite) images.
no code implementations • 28 Apr 2020 • Rodrigo Santa Cruz, Anoop Cherian, Basura Fernando, Dylan Campbell, Stephen Gould
This paper presents a framework to recognize temporal compositions of atomic actions in videos.
1 code implementation • 15 Mar 2020 • Liu Liu, Dylan Campbell, Hongdong Li, Dingfu Zhou, Xibin Song, Ruigang Yang
Conventional absolute camera pose via a Perspective-n-Point (PnP) solver often assumes that the correspondences between 2D image pixels and 3D points are given.
no code implementations • 26 Feb 2020 • Shihao Jiang, Dylan Campbell, Miaomiao Liu, Stephen Gould, Richard Hartley
We address the problem of joint optical flow and camera motion estimation in rigid scenes by incorporating geometric constraints into an unsupervised deep learning framework.
1 code implementation • 16 Dec 2019 • Ali Cheraghian, Shafin Rahman, Dylan Campbell, Lars Petersson
This paper extends, for the first time, transductive Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL) approaches to the domain of 3D point cloud classification.
1 code implementation • 11 Sep 2019 • Stephen Gould, Richard Hartley, Dylan Campbell
We show how these declarative processing nodes can be implemented in the popular PyTorch deep learning software library allowing declarative and imperative nodes to co-exist within the same network.
no code implementations • 15 Jul 2019 • Ali Cheraghian, Shafin Rahman, Dylan Campbell, Lars Petersson
In this paper, we therefore propose a loss to specifically address the hubness problem.
no code implementations • CVPR 2019 • Dylan Campbell, Lars Petersson, Laurent Kneip, Hongdong Li, Stephen Gould
Determining the position and orientation of a calibrated camera from a single image with respect to a 3D model is an essential task for many applications.
no code implementations • ICCV 2017 • Dylan Campbell, Lars Petersson, Laurent Kneip, Hongdong Li
Estimating the 6-DoF pose of a camera from a single image relative to a pre-computed 3D point-set is an important task for many computer vision applications.
no code implementations • 11 May 2016 • Jiaolong Yang, Hongdong Li, Dylan Campbell, Yunde Jia
The evaluation demonstrates that the proposed method is able to produce reliable registration results regardless of the initialization.
Ranked #6 on Point Cloud Registration on FP-O-H
no code implementations • CVPR 2016 • Dylan Campbell, Lars Petersson
Gaussian mixture alignment is a family of approaches that are frequently used for robustly solving the point-set registration problem.
1 code implementation • ICCV 2015 • Dylan Campbell, Lars Petersson
This paper presents a framework for rigid point-set registration and merging using a robust continuous data representation.