no code implementations • 24 Oct 2024 • Hemanth Saratchandran, Jianqiao Zheng, Yiping Ji, Wenbo Zhang, Simon Lucey
This paper challenges the conventional belief that softmax attention in transformers is effective primarily because it generates a probability distribution for attention allocation.
no code implementations • 5 Sep 2024 • Hemanth Saratchandran, Thomas X. Wang, Simon Lucey
In this article, we introduce a novel normalization technique for neural network weight matrices, which we term weight conditioning.
no code implementations • 17 Jul 2024 • Shin-Fang Chng, Ravi Garg, Hemanth Saratchandran, Simon Lucey
This paper tackles the simultaneous optimization of pose and Neural Radiance Fields (NeRF).
no code implementations • 19 Jun 2024 • Nathaniel Chodosh, Anish Madan, Deva Ramanan, Simon Lucey
To achieve this, we take inspiration from recent novel view synthesis methods and pose the reconstruction problem as a global optimization, minimizing the distance between our predicted surface and the input LiDAR scans.
1 code implementation • 1 Apr 2024 • Jianqiao Zheng, Xueqian Li, Simon Lucey
By contrast, convolutional neural networks (CNNs) have an architectural inductive bias enabling them to perform well on such problems.
no code implementations • 28 Mar 2024 • Cameron Gordon, Lachlan Ewen MacDonald, Hemanth Saratchandran, Simon Lucey
We instead present a strategy for the optimization of runtime deep implicit functions for single-instance signals through a Decoder-Only randomly projected Hypernetwork (D'OH).
no code implementations • CVPR 2024 • Hemanth Saratchandran, Sameera Ramasinghe, Simon Lucey
In the realm of computer vision, Neural Fields have gained prominence as a contemporary tool harnessing neural networks for signal representation.
no code implementations • 28 Mar 2024 • Yiping Ji, Hemanth Saratchandran, Cameron Gordon, Zeyu Zhang, Simon Lucey
Low-rank decomposition has emerged as a vital tool for enhancing parameter efficiency in neural network architectures, gaining traction across diverse applications in machine learning.
1 code implementation • 9 Mar 2024 • Xueqian Li, Simon Lucey
In contrast to current state-of-the-art methods, such as NSFP [25], which employ deep implicit neural functions for modeling scene flow, we present a novel approach that utilizes classical kernel representations.
no code implementations • CVPR 2024 • Jenny Seidenschwarz, Aljoša Ošep, Francesco Ferroni, Simon Lucey, Laura Leal-Taixé
Recent results suggest that heuristic-based clustering methods in conjunction with object trackers can be used to pseudo-label instances of moving objects and use these as supervisory signals to train 3D object detectors in Lidar data without manual supervision.
no code implementations • 13 Feb 2024 • Shin-Fang Chng, Hemanth Saratchandran, Simon Lucey
Implicit neural representations have emerged as a powerful technique for encoding complex continuous multidimensional signals as neural networks, enabling a wide range of applications in computer vision, robotics, and geometry.
no code implementations • 8 Feb 2024 • Hemanth Saratchandran, Sameera Ramasinghe, Violetta Shevchenko, Alexander Long, Simon Lucey
Implicit Neural Representations (INRs) have gained popularity for encoding signals as compact, differentiable entities.
no code implementations • 7 Feb 2024 • Hemanth Saratchandran, Shin-Fang Chng, Simon Lucey
In this paper, we aim to address this gap by providing a theoretical understanding of periodically activated networks through an analysis of their Neural Tangent Kernel (NTK).
no code implementations • 5 Feb 2024 • Hemanth Saratchandran, Shin-Fang Chng, Simon Lucey
Physics-informed neural networks (PINNs) offer a promising avenue for tackling both forward and inverse problems in partial differential equations (PDEs) by incorporating deep learning with fundamental physics principles.
1 code implementation • 23 Jan 2024 • Jianqiao Zheng, Xueqian Li, Simon Lucey
Training vision transformer networks on small datasets poses challenges.
1 code implementation • CVPR 2024 • Mosam Dabhi, Laszlo A. Jeni, Simon Lucey
The lifting of 3D structure and camera from 2D landmarks is at the cornerstone of the entire discipline of computer vision.
Ranked #1 on 3D Facial Landmark Localization on H3WB
1 code implementation • 16 Oct 2023 • Kavisha Vidanapathirana, Shin-Fang Chng, Xueqian Li, Simon Lucey
The test-time optimization of scene flow - using a coordinate network as a neural prior - has gained popularity due to its simplicity, lack of dataset bias, and state-of-the-art performance.
1 code implementation • 1 Sep 2023 • Jianqiao Zheng, Xueqian Li, Sameera Ramasinghe, Simon Lucey
End-to-end trained per-point embeddings are an essential ingredient of any state-of-the-art 3D point cloud processing such as detection or alignment.
no code implementations • 24 May 2023 • Lachlan Ewen MacDonald, Jack Valmadre, Simon Lucey
We present a new approach to understanding the relationship between loss curvature and input-output model behaviour in deep learning.
no code implementations • ICCV 2023 • Hemanth Saratchandran, Shin-Fang Chng, Sameera Ramasinghe, Lachlan MacDonald, Simon Lucey
Coordinate networks are widely used in computer vision due to their ability to represent signals as compressed, continuous entities.
1 code implementation • ICCV 2023 • Xueqian Li, Jianqiao Zheng, Francesco Ferroni, Jhony Kaesemodel Pontes, Simon Lucey
Neural Scene Flow Prior (NSFP) is of significant interest to the vision community due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with dense lidar points.
Ranked #6 on Self-supervised Scene Flow Estimation on Argoverse 2
no code implementations • 4 Apr 2023 • Nathaniel Chodosh, Deva Ramanan, Simon Lucey
Popular benchmarks for self-supervised LiDAR scene flow (stereoKITTI, and FlyingThings3D) have unrealistic rates of dynamic motion, unrealistic correspondences, and unrealistic sampling patterns.
no code implementations • CVPR 2023 • Chaoyang Wang, Lachlan Ewen MacDonald, Laszlo A. Jeni, Simon Lucey
In this paper we present a new method for deformable NeRF that can directly use optical flow as supervision.
no code implementations • 10 Mar 2023 • Sameera Ramasinghe, Hemanth Saratchandran, Violetta Shevchenko, Simon Lucey
Modelling dynamical systems is an integral component for understanding the natural world.
no code implementations • ICCV 2023 • MingFang Chang, Akash Sharma, Michael Kaess, Simon Lucey
We address outdoor Neural Radiance Fields (NeRF) with LiDAR maps.
no code implementations • NeurIPS 2023 • Lachlan Ewen MacDonald, Jack Valmadre, Hemanth Saratchandran, Simon Lucey
We introduce a general theoretical framework, designed for the study of gradient optimisation of deep neural networks, that encompasses ubiquitous architecture choices including batch normalisation, weight normalisation and skip connections.
2 code implementations • 4 Oct 2022 • Mosam Dabhi, Chaoyang Wang, Tim Clifford, Laszlo Attila Jeni, Ian R. Fasel, Simon Lucey
Our Multi-view Bootstrapping in the Wild (MBW) approach demonstrates impressive results on standard human datasets, as well as tigers, cheetahs, fish, colobus monkeys, chimpanzees, and flamingos from videos captured casually in a zoo.
3D Reconstruction Semi-supervised 2D and 3D landmark labeling +1
no code implementations • 1 Sep 2022 • Cameron Gordon, Shin-Fang Chng, Lachlan MacDonald, Simon Lucey
The role of quantization within implicit/coordinate neural networks is still not fully understood.
no code implementations • 17 Jun 2022 • Sameera Ramasinghe, Lachlan MacDonald, Moshiur Farazi, Hemanth Saratchandran, Simon Lucey
Characterizing the remarkable generalization properties of over-parameterized neural networks remains an open problem.
1 code implementation • 18 May 2022 • Jianqiao Zheng, Sameera Ramasinghe, Xueqian Li, Simon Lucey
It is well noted that coordinate-based MLPs benefit -- in terms of preserving high-frequency information -- through the encoding of coordinate positions as an array of Fourier features.
2 code implementations • 12 Apr 2022 • Shin-Fang Chng, Sameera Ramasinghe, Jamie Sherrah, Simon Lucey
Despite Neural Radiance Fields (NeRF) showing compelling results in photorealistic novel views synthesis of real-world scenes, most existing approaches require accurate prior camera poses.
no code implementations • CVPR 2022 • Ming-Fang Chang, Yipu Zhao, Rajvi Shah, Jakob J. Engel, Michael Kaess, Simon Lucey
We address the problem of map sparsification for long-term visual localization.
no code implementations • 1 Feb 2022 • Sameera Ramasinghe, Lachlan MacDonald, Simon Lucey
We show that typical implicit regularization assumptions for deep neural networks (for regression) do not hold for coordinate-MLPs, a family of MLPs that are now ubiquitous in computer vision for representing high-frequency signals.
no code implementations • CVPR 2022 • Chaoyang Wang, Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey
Here, we propose a neural trajectory prior to capture continuous spatio-temporal information without the need for offline data.
no code implementations • 21 Dec 2021 • Sameera Ramasinghe, Simon Lucey
We propose a novel method to enhance the performance of coordinate-MLPs by learning instance-specific positional embeddings.
1 code implementation • 30 Nov 2021 • Sameera Ramasinghe, Simon Lucey
Coordinate-MLPs are emerging as an effective tool for modeling multidimensional continuous signals, overcoming many drawbacks associated with discrete grid-based approximations.
1 code implementation • CVPR 2022 • Lachlan Ewen MacDonald, Sameera Ramasinghe, Simon Lucey
Our framework enables the implementation of group convolutions over any finite-dimensional Lie group.
1 code implementation • NeurIPS 2021 • Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey
A central innovation here is the inclusion of a neural scene flow prior, which uses the architecture of neural networks as a new type of implicit regularizer.
Ranked #3 on Self-supervised Scene Flow Estimation on Argoverse 2
no code implementations • 22 Oct 2021 • Mosam Dabhi, Chaoyang Wang, Kunal Saluja, Laszlo Jeni, Ian Fasel, Simon Lucey
Multi-view triangulation is the gold standard for 3D reconstruction from 2D correspondences given known calibration and sufficient views.
1 code implementation • 6 Jul 2021 • Jianqiao Zheng, Sameera Ramasinghe, Simon Lucey
It is well noted that coordinate based MLPs benefit -- in terms of preserving high-frequency information -- through the encoding of coordinate positions as an array of Fourier features.
no code implementations • 28 May 2021 • George Cazenavette, Simon Lucey
Borrowing from the transformer models that revolutionized the field of natural language processing, self-supervised feature learning for visual tasks has also seen state-of-the-art success using these extremely deep, isotropic networks.
no code implementations • 12 May 2021 • Chaoyang Wang, Ben Eckart, Simon Lucey, Orazio Gallo
Recent approaches to render photorealistic views from a limited set of photographs have pushed the boundaries of our interactions with pictures of static scenes.
1 code implementation • CVPR 2022 • Damien Teney, Ehsan Abbasnejad, Simon Lucey, Anton Van Den Hengel
The method - the first to evade the simplicity bias - highlights the need for a better understanding and control of inductive biases in deep learning.
4 code implementations • ICCV 2021 • Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, Simon Lucey
In this paper, we propose Bundle-Adjusting Neural Radiance Fields (BARF) for training NeRF from imperfect (or even unknown) camera poses -- the joint problem of learning neural 3D representations and registering camera frames.
no code implementations • CVPR 2021 • Chaoyang Wang, Simon Lucey
Recent success in casting Non-rigid Structure from Motion (NRSfM) as an unsupervised deep learning problem has raised fundamental questions about what novelty in NRSfM prior could the deep learning offer.
no code implementations • 10 Mar 2021 • Calvin Murdock, George Cazenavette, Simon Lucey
In comparison to classical shallow representation learning techniques, deep neural networks have achieved superior performance in nearly every application benchmark.
no code implementations • CVPR 2021 • George Cazenavette, Calvin Murdock, Simon Lucey
Despite their unmatched performance, deep neural networks remain susceptible to targeted attacks by nearly imperceptible levels of adversarial noise.
no code implementations • 31 Oct 2020 • Jhony Kaesemodel Pontes, James Hays, Simon Lucey
Our proposed objective function can be used with or without learning---as a self-supervisory signal to learn scene flow representations, or as a non-learning-based method in which the scene flow is optimized during runtime.
1 code implementation • NeurIPS 2020 • Chen-Hsuan Lin, Chaoyang Wang, Simon Lucey
Dense 3D object reconstruction from a single image has recently witnessed remarkable advances, but supervising neural networks with ground-truth 3D shapes is impractical due to the laborious process of creating paired image-shape datasets.
3D Object Reconstruction From A Single Image 3D Reconstruction
2 code implementations • 19 Oct 2020 • Vinit Sarode, Animesh Dhagat, Rangaprasad Arun Srivatsan, Nicolas Zevallos, Simon Lucey, Howie Choset
We demonstrate these improvements on synthetic and real-world datasets.
no code implementations • 8 Sep 2020 • Hunter Goforth, Xiaoyan Hu, Michael Happold, Simon Lucey
We address the problem of estimating the pose and shape of vehicles from LiDAR scans, a common problem faced by the autonomous vehicle community.
1 code implementation • CVPR 2021 • Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey
We address the generalization ability of recent learning-based point cloud registration methods.
no code implementations • CVPR 2020 • Calvin Murdock, Simon Lucey
Choosing a deep neural network architecture is a fundamental problem in applications that require balancing performance and parameter efficiency.
no code implementations • CVPR 2020 • Nathaniel Chodosh, Simon Lucey
In this work we argue that for some types of inverse problems the CNN approximation breaks down leading to poor performance.
no code implementations • 19 Mar 2020 • Shubham Agrawal, Anuj Pahuja, Simon Lucey
What's the most accurate 3D model of your face you can obtain while sitting at your desk?
1 code implementation • 27 Jan 2020 • Chaoyang Wang, Chen-Hsuan Lin, Simon Lucey
The recovery of 3D shape and pose from 2D landmarks stemming from a large ensemble of images can be viewed as a non-rigid structure from motion (NRSfM) problem.
1 code implementation • 12 Dec 2019 • Vinit Sarode, Xueqian Li, Hunter Goforth, Yasuhiro Aoki, Animesh Dhagat, Rangaprasad Arun Srivatsan, Simon Lucey, Howie Choset
We perform extensive simulation and real-world experiments to validate the efficacy of our approach and compare the performance with state-of-art approaches.
3 code implementations • CVPR 2019 • Ming-Fang Chang, John Lambert, Patsorn Sangkloy, Jagjeet Singh, Slawomir Bak, Andrew Hartnett, De Wang, Peter Carr, Simon Lucey, Deva Ramanan, James Hays
In our baseline experiments, we illustrate how detailed map information such as lane direction, driveable area, and ground height improves the accuracy of 3D object tracking and motion forecasting.
6 code implementations • 21 Aug 2019 • Vinit Sarode, Xueqian Li, Hunter Goforth, Yasuhiro Aoki, Rangaprasad Arun Srivatsan, Simon Lucey, Howie Choset
PointNet has recently emerged as a popular representation for unstructured point cloud data, allowing application of deep learning to tasks such as object detection, segmentation and shape completion.
no code implementations • ICCV 2019 • Chaoyang Wang, Chen Kong, Simon Lucey
This alleviates the data bottleneck, which is one of the major concern for supervised methods.
Ranked #21 on Weakly-supervised 3D Human Pose Estimation on Human3.6M
no code implementations • ICCV 2019 • Chen Kong, Simon Lucey
Current non-rigid structure from motion (NRSfM) algorithms are mainly limited with respect to: (i) the number of images, and (ii) the type of shape variability they can handle.
no code implementations • 30 Jul 2019 • Chen Kong, Simon Lucey
Non-Rigid Structure from Motion (NRSfM) refers to the problem of reconstructing cameras and the 3D point cloud of a non-rigid object from an ensemble of images with 2D correspondences.
1 code implementation • 7 May 2019 • Tejas Khot, Shubham Agrawal, Shubham Tulsiani, Christoph Mertz, Simon Lucey, Martial Hebert
We demonstrate our ability to learn MVS without 3D supervision using a real dataset, and show that each component of our proposed robust loss results in a significant improvement.
no code implementations • 25 Apr 2019 • Chaoyang Wang, Simon Lucey, Federico Perazzi, Oliver Wang
We present a fully data-driven method to compute depth from diverse monocular video sequences that contain large amounts of non-rigid objects, e. g., people.
1 code implementation • CVPR 2019 • Chen-Hsuan Lin, Oliver Wang, Bryan C. Russell, Eli Shechtman, Vladimir G. Kim, Matthew Fisher, Simon Lucey
In this paper, we address the problem of 3D object mesh reconstruction from RGB videos.
7 code implementations • CVPR 2019 • Yasuhiro Aoki, Hunter Goforth, Rangaprasad Arun Srivatsan, Simon Lucey
To date, the successful application of PointNet to point cloud registration has remained elusive.
1 code implementation • 28 Feb 2019 • Chen Kong, Simon Lucey
All current non-rigid structure from motion (NRSfM) algorithms are limited with respect to: (i) the number of images, and (ii) the type of shape variability they can handle.
no code implementations • 23 Mar 2018 • Nathaniel Chodosh, Chaoyang Wang, Simon Lucey
In this paper we consider the problem of estimating a dense depth map from a set of sparse LiDAR points.
no code implementations • 22 Mar 2018 • Hunter Goforth, Simon Lucey
We present a method of temporally-invariant image registration for outdoor scenes, with invariance across time of day, across seasonal variations, and across decade-long periods, for low- and high-texture scenes.
1 code implementation • ECCV 2018 • Calvin Murdock, Ming-Fang Chang, Simon Lucey
Despite a lack of theoretical understanding, deep neural networks have achieved unparalleled performance in a wide range of applications.
2 code implementations • CVPR 2018 • Chen-Hsuan Lin, Ersin Yumer, Oliver Wang, Eli Shechtman, Simon Lucey
We address the problem of finding realistic geometric corrections to a foreground object such that it appears natural when composited into a background image.
no code implementations • 7 Dec 2017 • Chen Huang, Chen Kong, Simon Lucey
Stochastic Gradient Descent (SGD) is the central workhorse for training modern CNNs.
no code implementations • 7 Dec 2017 • Chen Kong, Simon Lucey
Since their inception, CNNs have utilized some type of striding operator to reduce the overlap of receptive fields and spatial dimensions.
1 code implementation • CVPR 2018 • Chaoyang Wang, Jose Miguel Buenaposada, Rui Zhu, Simon Lucey
The ability to predict depth from a single image - using recent advances in CNNs - is of increasing interest to the vision community.
no code implementations • 30 Nov 2017 • Rui Zhu, Chaoyang Wang, Chen-Hsuan Lin, Ziyan Wang, Simon Lucey
More recently, excellent results have been attained through the application of photometric bundle adjustment (PBA) methods -- which directly minimize the photometric error across frames.
1 code implementation • 29 Nov 2017 • Jhony K. Pontes, Chen Kong, Sridha Sridharan, Simon Lucey, Anders Eriksson, Clinton Fookes
One challenge that remains open in 3D deep learning is how to efficiently represent 3D data to feed deep networks.
no code implementations • 4 Nov 2017 • Rui Zhu, Chaoyang Wang, Chen-Hsuan Lin, Ziyan Wang, Simon Lucey
Reconstructing 3D shapes from a sequence of images has long been a problem of interest in computer vision.
no code implementations • ICCV 2017 • Rui Zhu, Hamed Kiani Galoogahi, Chaoyang Wang, Simon Lucey
An emerging problem in computer vision is the reconstruction of 3D shape and pose of an object from a single image.
no code implementations • ICCV 2017 • Chen Huang, Simon Lucey, Deva Ramanan
Our fundamental insight is to take an adaptive approach, where easy frames are processed with cheap features (such as pixel values), while challenging frames are processed with invariant but expensive deep features.
no code implementations • 23 Jul 2017 • Jhony K. Pontes, Chen Kong, Anders Eriksson, Clinton Fookes, Sridha Sridharan, Simon Lucey
3D reconstruction from 2D images is a central problem in computer vision.
no code implementations • 15 Jul 2017 • Rui Zhu, Hamed Kiani Galoogahi, Chaoyang Wang, Simon Lucey
An emerging problem in computer vision is the reconstruction of 3D shape and pose of an object from a single image.
no code implementations • CVPR 2017 • Chen Kong, Chen-Hsuan Lin, Simon Lucey
A common strategy in dictionary learning to encourage generalization is to allow for linear combinations of dictionary elements.
3 code implementations • 21 Jun 2017 • Chen-Hsuan Lin, Chen Kong, Simon Lucey
Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones.
no code implementations • 13 Jun 2017 • Iman Abbasnejad, Sridha Sridharan, Simon Denman, Clinton Fookes, Simon Lucey
In this paper the problem of complex event detection in the continuous domain (i. e. events with unknown starting and ending locations) is addressed.
no code implementations • 19 May 2017 • Chaoyang Wang, Hamed Kiani Galoogahi, Chen-Hsuan Lin, Simon Lucey
In this paper we present a new approach for efficient regression based object tracking which we refer to as Deep- LK.
no code implementations • 23 Apr 2017 • Christopher Ham, Simon Lucey, Surya Singh
In this paper, we show that an ordinary inverse compositional formulation does not work for warps of this type of parameterization due to ill-conditioning of its partial derivatives.
1 code implementation • ICCV 2017 • Hamed Kiani Galoogahi, Ashton Fagg, Chen Huang, Deva Ramanan, Simon Lucey
In this paper, we propose the first higher frame rate video dataset (called Need for Speed - NfS) and benchmark for visual object tracking.
1 code implementation • ICCV 2017 • Hamed Kiani Galoogahi, Ashton Fagg, Simon Lucey
Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations.
no code implementations • 16 Dec 2016 • Ashton Fagg, Simon Lucey, Sridha Sridharan
In this paper, we present our method for enabling dense SDM to run at over 90 FPS on a mobile device.
1 code implementation • CVPR 2017 • Chen-Hsuan Lin, Simon Lucey
In this paper, we establish a theoretical connection between the classical Lucas & Kanade (LK) algorithm and the emerging topic of Spatial Transformer Networks (STNs).
no code implementations • 5 Aug 2016 • Hatem Alismail, Brett Browning, Simon Lucey
We propose a novel algorithm for the joint refinement of structure and motion parameters from image data directly without relying on fixed and known correspondences.
no code implementations • CVPR 2016 • Chen Kong, Simon Lucey
Many non-rigid 3D structures are not modelled well through a low-rank subspace assumption.
no code implementations • 4 Apr 2016 • Hatem Alismail, Brett Browning, Simon Lucey
Our approach utilizes an efficient binary descriptor, which we call Bit-Planes, and show how it can be used in the gradient-based optimization required by direct methods.
1 code implementation • 29 Mar 2016 • Chen-Hsuan Lin, Rui Zhu, Simon Lucey
In this paper, we present a new approach, referred to as the Conditional LK algorithm, which: (i) directly learns linear models that predict geometric displacement as a function of appearance, and (ii) employs a novel strategy for ensuring that the generative pixel independence assumption can still be taken advantage of.
no code implementations • 31 Jan 2016 • Hatem Alismail, Brett Browning, Simon Lucey
Binary descriptors have been instrumental in the recent evolution of computationally efficient sparse image alignment algorithms.
no code implementations • 4 Sep 2015 • Iman Abbasnejad, Sridha Sridharan, Simon Denman, Clinton Fookes, Simon Lucey
A popular approach in this regard is to represent a sequence using a bag of words (BOW) representation due to its: (i) fixed dimensionality irrespective of the sequence length, and (ii) its ability to compactly model the statistics in the sequence.
no code implementations • ICCV 2015 • Hilton Bristow, Jack Valmadre, Simon Lucey
Determining dense semantic correspondences across objects and scenes is a difficult problem that underpins many higher-level computer vision algorithms.
no code implementations • 8 Jul 2014 • Hilton Bristow, Simon Lucey
Gradient-descent methods have exhibited fast and reliable performance for image alignment in the facial domain, but have largely been ignored by the broader vision community.
no code implementations • 10 Jun 2014 • Hilton Bristow, Simon Lucey
We discuss a range of optimization methods for solving the convolutional sparse coding objective, and the properties that make each method suitable for different applications.
2 code implementations • 10 Jun 2014 • Hilton Bristow, Simon Lucey
Linear Support Vector Machines trained on HOG features are now a de facto standard across many visual perception tasks.
no code implementations • CVPR 2014 • Yingying Zhu, Dong Huang, Fernando de la Torre, Simon Lucey
The task of estimating complex non-rigid 3D motion through a monocular camera is of increasing interest to the wider scientific community.
no code implementations • CVPR 2015 • Hamed Kiani Galoogahi, Terence Sim, Simon Lucey
In this paper, we propose a novel approach to correlation filter estimation that: (i) takes advantage of inherent computational redundancies in the frequency domain, and (ii) dramatically reduces boundary effects.
no code implementations • 28 Mar 2014 • Jack Valmadre, Sridha Sridharan, Simon Lucey
Computer vision is increasingly becoming interested in the rapid estimation of object detectors.
no code implementations • CVPR 2013 • Hilton Bristow, Anders Eriksson, Simon Lucey
Sparse coding has become an increasingly popular method in learning and vision for a variety of classification, reconstruction and coding tasks.