Search Results for author: Mehrtash Harandi

Found 100 papers, 21 papers with code

On Modulating the Gradient for Meta-Learning

1 code implementation ECCV 2020 Christian Simon, Piotr Koniusz, Richard Nock, Mehrtash Harandi

Inspired by optimization techniques, we propose a novel meta-learning algorithm with gradient modulation to encourage fast-adaptation of neural networks in the absence of abundant data.

Meta-Learning

Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks

no code implementations11 Jan 2024 Jing Wu, Mehrtash Harandi

Machine unlearning has become a pivotal task to erase the influence of data from a trained model.

Machine Unlearning

LaViP:Language-Grounded Visual Prompts

no code implementations18 Dec 2023 Nilakshan Kunananthaseelan, Jing Zhang, Mehrtash Harandi

We introduce a language-grounded visual prompting method to adapt the visual encoder of vision-language models for downstream tasks.

Few-Shot Learning Transfer Learning +1

Unleash Data Generation for Efficient and Effective Data-free Knowledge Distillation

no code implementations30 Sep 2023 Minh-Tuan Tran, Trung Le, Xuan-May Le, Mehrtash Harandi, Quan Hung Tran, Dinh Phung

By reinitializing the noisy layer in each iteration, we aim to facilitate the generation of diverse samples while still retaining the method's efficiency, thanks to the ease of learning provided by LTE.

Data-free Knowledge Distillation

Hyperbolic Audio-visual Zero-shot Learning

no code implementations ICCV 2023 Jie Hong, Zeeshan Hayder, Junlin Han, Pengfei Fang, Mehrtash Harandi, Lars Petersson

Audio-visual zero-shot learning aims to classify samples consisting of a pair of corresponding audio and video sequences from classes that are not present during training.

GZSL Video Classification

L3DMC: Lifelong Learning using Distillation via Mixed-Curvature Space

1 code implementation31 Jul 2023 Kaushik Roy, Peyman Moghadam, Mehrtash Harandi

To address the problem, we propose a distillation strategy named L3DMC that operates on mixed-curvature spaces to preserve the already-learned knowledge by modeling and maintaining complex geometrical structures.

Image Classification Medical Image Classification +1

Subspace Distillation for Continual Learning

1 code implementation31 Jul 2023 Kaushik Roy, Christian Simon, Peyman Moghadam, Mehrtash Harandi

To mitigate forgetting prior knowledge, we propose a novel knowledge distillation technique that takes into the account the manifold structure of the latent/output space of a neural network in learning novel tasks.

Continual Learning Knowledge Distillation +1

EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos

1 code implementation21 Jul 2023 Ruyi Zha, Xuelian Cheng, Hongdong Li, Mehrtash Harandi, ZongYuan Ge

We constrain the learned shape by tailoring multiple regularization strategies and disentangling geometry and appearance.

Surface Reconstruction

Hyperbolic Geometry in Computer Vision: A Survey

no code implementations21 Apr 2023 Pengfei Fang, Mehrtash Harandi, Trung Le, Dinh Phung

Hyperbolic geometry, a Riemannian manifold endowed with constant sectional negative curvature, has been considered an alternative embedding space in many learning scenarios, \eg, natural language processing, graph learning, \etc, as a result of its intriguing property of encoding the data's hierarchical structure (like irregular graph or tree-likeness data).

Graph Learning Image Classification

Exploring Data Geometry for Continual Learning

no code implementations CVPR 2023 Zhi Gao, Chen Xu, Feng Li, Yunde Jia, Mehrtash Harandi, Yuwei Wu

Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data, and prevents forgetting by keeping geometric structures of old data into account.

Continual Learning

Vector Quantized Wasserstein Auto-Encoder

no code implementations12 Feb 2023 Tung-Long Vuong, Trung Le, He Zhao, Chuanxia Zheng, Mehrtash Harandi, Jianfei Cai, Dinh Phung

Learning deep discrete latent presentations offers a promise of better symbolic and summarized abstractions that are more useful to subsequent downstream tasks.

Clustering Image Reconstruction

Energy-based Self-Training and Normalization for Unsupervised Domain Adaptation

no code implementations ICCV 2023 Samitha Herath, Basura Fernando, Ehsan Abbasnejad, Munawar Hayat, Shahram Khadivi, Mehrtash Harandi, Hamid Rezatofighi, Gholamreza Haffari

EBL can be used to improve the instance selection for a self-training task on the unlabelled target domain, and 2. alignment and normalizing energy scores can learn domain-invariant representations.

Unsupervised Domain Adaptation

PointCaM: Cut-and-Mix for Open-Set Point Cloud Learning

1 code implementation5 Dec 2022 Jie Hong, Shi Qiu, Weihao Li, Saeed Anwar, Mehrtash Harandi, Nick Barnes, Lars Petersson

Specifically, we use the Unknown-Point Simulator to simulate out-of-distribution data in the training stage by manipulating the geometric context of partial known data.

Multimorbidity Content-Based Medical Image Retrieval Using Proxies

no code implementations22 Nov 2022 Yunyan Xing, Benjamin J. Meyer, Mehrtash Harandi, Tom Drummond, ZongYuan Ge

Medical imaging data, such as radiology images, are often multimorbidity; a single sample may have more than one pathology present.

Content-Based Image Retrieval Decision Making +3

A Differentiable Distance Approximation for Fairer Image Classification

1 code implementation9 Oct 2022 Nicholas Rosa, Tom Drummond, Mehrtash Harandi

We demonstrate that our approach improves the fairness of AI models in varied task and dataset scenarios, whilst still maintaining a high level of classification accuracy.

Classification Fairness +1

Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation

1 code implementation16 Sep 2022 Himashi Peiris, Munawar Hayat, Zhaolin Chen, Gary Egan, Mehrtash Harandi

As intensities of MRI volumes are inconsistent across institutes, it is essential to extract universal features of multi-modal MRIs to precisely segment brain tumors.

Brain Tumor Segmentation Tumor Segmentation

Learning Deep Optimal Embeddings with Sinkhorn Divergences

no code implementations14 Sep 2022 Soumava Kumar Roy, Yan Han, Mehrtash Harandi, Lars Petersson

Deep Metric Learning algorithms aim to learn an efficient embedding space to preserve the similarity relationships among the input data.

Fine-Grained Image Recognition Image Classification +1

Concealing Sensitive Samples against Gradient Leakage in Federated Learning

1 code implementation13 Sep 2022 Jing Wu, Munawar Hayat, Mingyi Zhou, Mehrtash Harandi

Federated Learning (FL) is a distributed learning paradigm that enhances users privacy by eliminating the need for clients to share raw, private data with the server.

Federated Learning Stochastic Optimization

Curved Geometric Networks for Visual Anomaly Recognition

no code implementations2 Aug 2022 Jie Hong, Pengfei Fang, Weihao Li, Junlin Han, Lars Petersson, Mehrtash Harandi

Learning a latent embedding to understand the underlying nature of data distribution is often formulated in Euclidean spaces with zero curvature.

Anomaly Detection Out of Distribution (OOD) Detection +1

Deep Laparoscopic Stereo Matching with Transformers

1 code implementation25 Jul 2022 Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Tom Drummond, Zhiyong Wang, ZongYuan Ge

The self-attention mechanism, successfully employed with the transformer structure is shown promise in many computer vision tasks including image recognition, and object detection.

object-detection Object Detection +2

Multi-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstruction

no code implementations18 Jul 2022 Mevan Ekanayake, Kamlesh Pawar, Mehrtash Harandi, Gary Egan, Zhaolin Chen

Convolutional neural network (CNN) models are widely utilized for accelerated MRI reconstruction, but those models are limited in capturing global correlations due to the intrinsic locality of the convolution operation.

De-aliasing MRI Reconstruction

Rethinking Generalization in Few-Shot Classification

1 code implementation15 Jun 2022 Markus Hiller, Rongkai Ma, Mehrtash Harandi, Tom Drummond

Single image-level annotations only correctly describe an often small subset of an image's content, particularly when complex real-world scenes are depicted.

Classification Few-Shot Image Classification +1

On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation

no code implementations15 Jun 2022 Markus Hiller, Mehrtash Harandi, Tom Drummond

Inspired by the concept of preconditioning, we propose a novel method to increase adaptation speed for gradient-based meta-learning methods without incurring extra parameters.

Meta-Learning

GOSS: Towards Generalized Open-set Semantic Segmentation

no code implementations23 Mar 2022 Jie Hong, Weihao Li, Junlin Han, Jiyang Zheng, Pengfei Fang, Mehrtash Harandi, Lars Petersson

In this paper, we present and study a new image segmentation task, called Generalized Open-set Semantic Segmentation (GOSS).

Clustering Image Segmentation +2

Implicit Motion Handling for Video Camouflaged Object Detection

1 code implementation CVPR 2022 Xuelian Cheng, Huan Xiong, Deng-Ping Fan, Yiran Zhong, Mehrtash Harandi, Tom Drummond, ZongYuan Ge

We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames.

Camouflaged Object Segmentation Motion Estimation +4

On Generalizing Beyond Domains in Cross-Domain Continual Learning

no code implementations CVPR 2022 Christian Simon, Masoud Faraki, Yi-Hsuan Tsai, Xiang Yu, Samuel Schulter, Yumin Suh, Mehrtash Harandi, Manmohan Chandraker

Humans have the ability to accumulate knowledge of new tasks in varying conditions, but deep neural networks often suffer from catastrophic forgetting of previously learned knowledge after learning a new task.

Continual Learning Knowledge Distillation

Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning

1 code implementation7 Dec 2021 Rongkai Ma, Pengfei Fang, Gil Avraham, Yan Zuo, Tianyu Zhu, Tom Drummond, Mehrtash Harandi

A principle way of achieving few-shot learning is to realize a model that can rapidly adapt to the context of a given task.

Few-Shot Learning Novel Concepts

Adaptive Poincaré Point to Set Distance for Few-Shot Classification

no code implementations3 Dec 2021 Rongkai Ma, Pengfei Fang, Tom Drummond, Mehrtash Harandi

To this end, we formulate the metric as a weighted sum on the tangent bundle of the hyperbolic space and develop a mechanism to obtain the weights adaptively and based on the constellation of the points.

Few-Shot Learning

A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation

1 code implementation26 Nov 2021 Himashi Peiris, Munawar Hayat, Zhaolin Chen, Gary Egan, Mehrtash Harandi

We propose a Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume.

Brain Tumor Segmentation Segmentation +2

Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model

no code implementations22 Nov 2021 Jing Zhang, Yuchao Dai, Mehrtash Harandi, Yiran Zhong, Nick Barnes, Richard Hartley

Uncertainty estimation has been extensively studied in recent literature, which can usually be classified as aleatoric uncertainty and epistemic uncertainty.

Attribute object-detection +1

Learning Online for Unified Segmentation and Tracking Models

no code implementations12 Nov 2021 Tianyu Zhu, Rongkai Ma, Mehrtash Harandi, Tom Drummond

A segmentation model cannot easily learn from prior information given in the visual tracking scenario.

Meta-Learning Visual Tracking

Meta-Learning for Multi-Label Few-Shot Classification

no code implementations26 Oct 2021 Christian Simon, Piotr Koniusz, Mehrtash Harandi

Even with the luxury of having abundant data, multi-label classification is widely known to be a challenging task to address.

Classification Few-Shot Learning +1

Towards a Robust Differentiable Architecture Search under Label Noise

no code implementations23 Oct 2021 Christian Simon, Piotr Koniusz, Lars Petersson, Yan Han, Mehrtash Harandi

Our empirical evaluations show that the noise injecting operation does not degrade the performance of the NAS algorithm if the data is indeed clean.

Neural Architecture Search

Dense Uncertainty Estimation

1 code implementation13 Oct 2021 Jing Zhang, Yuchao Dai, Mochu Xiang, Deng-Ping Fan, Peyman Moghadam, Mingyi He, Christian Walder, Kaihao Zhang, Mehrtash Harandi, Nick Barnes

Deep neural networks can be roughly divided into deterministic neural networks and stochastic neural networks. The former is usually trained to achieve a mapping from input space to output space via maximum likelihood estimation for the weights, which leads to deterministic predictions during testing.

Decision Making

IDENTIFYING CONCEALED OBJECTS FROM VIDEOS

no code implementations29 Sep 2021 Xuelian Cheng, Huan Xiong, Deng-Ping Fan, Yiran Zhong, Mehrtash Harandi, Tom Drummond, ZongYuan Ge

The proposed SLT-Net leverages on both short-term dynamics and long-term temporal consistency to detect concealed objects in continuous video frames.

object-detection Object Detection

Feature Correlation Aggregation: on the Path to Better Graph Neural Networks

no code implementations20 Sep 2021 Jieming Zhou, Tong Zhang, Pengfei Fang, Lars Petersson, Mehrtash Harandi

The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbors.

Semi-Supervised Metric Learning: A Deep Resurrection

no code implementations10 May 2021 Ujjal Kr Dutta, Mehrtash Harandi, Chellu Chandra Sekhar

In this paper, we address the problem of Semi-Supervised DML (SSDML) that tries to learn a metric using a few labeled examples, and abundantly available unlabeled examples.

Metric Learning

Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions

no code implementations22 Apr 2021 Jing Wu, Mingyi Zhou, Ce Zhu, Yipeng Liu, Mehrtash Harandi, Li Li

Recently, adversarial attack methods have been developed to challenge the robustness of machine learning models.

Adversarial Attack

On Learning the Geodesic Path for Incremental Learning

1 code implementation CVPR 2021 Christian Simon, Piotr Koniusz, Mehrtash Harandi

This in practice is done by minimizing the dissimilarity between current and previous responses of the network one way or another.

Incremental Learning Knowledge Distillation

Reinforced Attention for Few-Shot Learning and Beyond

no code implementations CVPR 2021 Jie Hong, Pengfei Fang, Weihao Li, Tong Zhang, Christian Simon, Mehrtash Harandi, Lars Petersson

Few-shot learning aims to correctly recognize query samples from unseen classes given a limited number of support samples, often by relying on global embeddings of images.

Few-Shot Learning Image Classification

Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning

no code implementations CVPR 2021 Ali Cheraghian, Shafin Rahman, Pengfei Fang, Soumava Kumar Roy, Lars Petersson, Mehrtash Harandi

Few-shot class incremental learning (FSCIL) portrays the problem of learning new concepts gradually, where only a few examples per concept are available to the learner.

Few-Shot Class-Incremental Learning Incremental Learning +2

Learning to Continually Learn Rapidly from Few and Noisy Data

1 code implementation6 Mar 2021 Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier, Christian Walder, Gabriela Ferraro, Hanna Suominen

Neural networks suffer from catastrophic forgetting and are unable to sequentially learn new tasks without guaranteed stationarity in data distribution.

Continual Learning Meta-Learning

Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation

no code implementations28 Feb 2021 Lie Ju, Xin Wang, Lin Wang, Dwarikanath Mahapatra, Xin Zhao, Mehrtash Harandi, Tom Drummond, Tongliang Liu, ZongYuan Ge

In this paper, we systematically discuss and define the two common types of label noise in medical images - disagreement label noise from inconsistency expert opinions and single-target label noise from wrong diagnosis record.

Benchmarking General Classification +3

Highway-Connection Classifier Networks for Plastic yet Stable Continual Learning

no code implementations1 Jan 2021 Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier, Christian Walder, Gabriela Ferraro, Hanna Suominen

Catastrophic forgetting occurs when a neural network is trained sequentially on multiple tasks – its weights will be continuously modified and as a result, the network will lose its ability in solving a previous task.

Continual Learning

Curvature Generation in Curved Spaces for Few-Shot Learning

no code implementations ICCV 2021 Zhi Gao, Yuwei Wu, Yunde Jia, Mehrtash Harandi

Few-shot learning describes the challenging problem of recognizing samples from unseen classes given very few labeled examples.

Few-Shot Learning

Kernel Methods in Hyperbolic Spaces

no code implementations ICCV 2021 Pengfei Fang, Mehrtash Harandi, Lars Petersson

However, working in hyperbolic spaces is not without difficulties as a result of its curved geometry (e. g., computing the Frechet mean of a set of points requires an iterative algorithm).

Few-Shot Learning Image Classification +5

Uncertainty-Aware Deep Calibrated Salient Object Detection

no code implementations10 Dec 2020 Jing Zhang, Yuchao Dai, Xin Yu, Mehrtash Harandi, Nick Barnes, Richard Hartley

Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy.

Object object-detection +2

Set Augmented Triplet Loss for Video Person Re-Identification

no code implementations2 Nov 2020 Pengfei Fang, Pan Ji, Lars Petersson, Mehrtash Harandi

Modern video person re-identification (re-ID) machines are often trained using a metric learning approach, supervised by a triplet loss.

Metric Learning Video-Based Person Re-Identification

Hierarchical Neural Architecture Search for Deep Stereo Matching

1 code implementation NeurIPS 2020 Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Tom Drummond, Hongdong Li, ZongYuan Ge

To reduce the human efforts in neural network design, Neural Architecture Search (NAS) has been applied with remarkable success to various high-level vision tasks such as classification and semantic segmentation.

Neural Architecture Search Semantic Segmentation +3

Channel Recurrent Attention Networks for Video Pedestrian Retrieval

no code implementations7 Oct 2020 Pengfei Fang, Pan Ji, Jieming Zhou, Lars Petersson, Mehrtash Harandi

Full attention, which generates an attention value per element of the input feature maps, has been successfully demonstrated to be beneficial in visual tasks.

Person Retrieval Retrieval

Unsupervised Deep Metric Learning via Orthogonality based Probabilistic Loss

no code implementations22 Aug 2020 Ujjal Kr Dutta, Mehrtash Harandi, Chellu Chandra Sekhar

As obtaining class labels in all applications is not feasible, we propose an unsupervised approach that learns a metric without making use of class labels.

Clustering Metric Learning

MTL2L: A Context Aware Neural Optimiser

1 code implementation18 Jul 2020 Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier, Christian Walder, Gabriela Ferraro, Hanna Suominen

Learning to learn (L2L) trains a meta-learner to assist the learning of a task-specific base learner.

Multi-Task Learning

Cross-Correlated Attention Networks for Person Re-Identification

no code implementations17 Jun 2020 Jieming Zhou, Soumava Kumar Roy, Pengfei Fang, Mehrtash Harandi, Lars Petersson

Deep neural networks need to make robust inference in the presence of occlusion, background clutter, pose and viewpoint variations -- to name a few -- when the task of person re-identification is considered.

Person Re-Identification

Affinity guided Geometric Semi-Supervised Metric Learning

no code implementations27 Feb 2020 Ujjal Kr Dutta, Mehrtash Harandi, Chellu Chandra Sekhar

In this paper, we revamp the forgotten classical Semi-Supervised Distance Metric Learning (SSDML) problem from a Riemannian geometric lens, to leverage stochastic optimization within a end-to-end deep framework.

Metric Learning Riemannian optimization +1

A Probabilistic approach for Learning Embeddings without Supervision

no code implementations17 Dec 2019 Ujjal Kr Dutta, Mehrtash Harandi, Chandra Sekhar Chellu

This restricts their applicability for large datasets in new applications where obtaining labels require extensive manual efforts and domain knowledge.

Clustering Metric Learning +1

Projective Subspace Networks For Few-Shot Learning

no code implementations ICLR 2019 Christian Simon, Piotr Koniusz, Mehrtash Harandi

Generalization from limited examples, usually studied under the umbrella of meta-learning, equips learning techniques with the ability to adapt quickly in dynamical environments and proves to be an essential aspect of lifelong learning.

Few-Shot Learning General Classification

Neural Collaborative Subspace Clustering

no code implementations24 Apr 2019 Tong Zhang, Pan Ji, Mehrtash Harandi, Wenbing Huang, Hongdong Li

We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of low-dimensional subspaces.

Clustering

Scalable Deep $k$-Subspace Clustering

no code implementations2 Nov 2018 Tong Zhang, Pan Ji, Mehrtash Harandi, Richard Hartley, Ian Reid

In this paper, we introduce a method that simultaneously learns an embedding space along subspaces within it to minimize a notion of reconstruction error, thus addressing the problem of subspace clustering in an end-to-end learning paradigm.

Clustering

Block Mean Approximation for Efficient Second Order Optimization

no code implementations16 Apr 2018 Yao Lu, Mehrtash Harandi, Richard Hartley, Razvan Pascanu

Advanced optimization algorithms such as Newton method and AdaGrad benefit from second order derivative or second order statistics to achieve better descent directions and faster convergence rates.

Devon: Deformable Volume Network for Learning Optical Flow

no code implementations20 Feb 2018 Yao Lu, Jack Valmadre, Heng Wang, Juho Kannala, Mehrtash Harandi, Philip H. S. Torr

State-of-the-art neural network models estimate large displacement optical flow in multi-resolution and use warping to propagate the estimation between two resolutions.

Optical Flow Estimation

Museum Exhibit Identification Challenge for Domain Adaptation and Beyond

no code implementations4 Feb 2018 Piotr Koniusz, Yusuf Tas, Hongguang Zhang, Mehrtash Harandi, Fatih Porikli, Rui Zhang

To achieve robust baselines, we build on a recent approach that aligns per-class scatter matrices of the source and target CNN streams [15].

Domain Adaptation Few-Shot Learning

Generalized Rank Pooling for Activity Recognition

no code implementations CVPR 2017 Anoop Cherian, Basura Fernando, Mehrtash Harandi, Stephen Gould

Most popular deep models for action recognition split video sequences into short sub-sequences consisting of a few frames; frame-based features are then pooled for recognizing the activity.

Action Recognition Riemannian optimization +1

Learning an Invariant Hilbert Space for Domain Adaptation

no code implementations CVPR 2017 Samitha Herath, Mehrtash Harandi, Fatih Porikli

This paper introduces a learning scheme to construct a Hilbert space (i. e., a vector space along its inner product) to address both unsupervised and semi-supervised domain adaptation problems.

Domain Adaptation Riemannian optimization +1

Generalized BackPropagation, Étude De Cas: Orthogonality

no code implementations17 Nov 2016 Mehrtash Harandi, Basura Fernando

This paper introduces an extension of the backpropagation algorithm that enables us to have layers with constrained weights in a deep network.

Dimensionality Reduction Fine-Grained Image Classification +1

Analyzing Linear Dynamical Systems: From Modeling to Coding and Learning

no code implementations3 Aug 2016 Wenbing Huang, Fuchun Sun, Lele Cao, Mehrtash Harandi

We then devise efficient algorithms to perform sparse coding and dictionary learning on the space of infinite-dimensional subspaces.

Dictionary Learning General Classification +5

Sparse Coding and Dictionary Learning With Linear Dynamical Systems

no code implementations CVPR 2016 Wenbing Huang, Fuchun Sun, Lele Cao, Deli Zhao, Huaping Liu, Mehrtash Harandi

To enhance the performance of LDSs, in this paper, we address the challenging issue of performing sparse coding on the space of LDSs, where both data and dictionary atoms are LDSs.

Dictionary Learning Video Classification

Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods

no code implementations20 May 2016 Mehrtash Harandi, Mathieu Salzmann, Richard Hartley

This lets us formulate dimensionality reduction as the problem of finding a projection that yields a low-dimensional manifold either with maximum discriminative power in the supervised scenario, or with maximum variance of the data in the unsupervised one.

Dimensionality Reduction

Going Deeper into Action Recognition: A Survey

no code implementations16 May 2016 Samitha Herath, Mehrtash Harandi, Fatih Porikli

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation.

Action Analysis Action Recognition +6

When VLAD met Hilbert

no code implementations CVPR 2016 Mehrtash Harandi, Mathieu Salzmann, Fatih Porikli

Vectors of Locally Aggregated Descriptors (VLAD) have emerged as powerful image/video representations that compete with or even outperform state-of-the-art approaches on many challenging visual recognition tasks.

General Classification

Riemannian Coding and Dictionary Learning: Kernels to the Rescue

no code implementations CVPR 2015 Mehrtash Harandi, Mathieu Salzmann

While sparse coding on non-flat Riemannian manifolds has recently become increasingly popular, existing solutions either are dedicated to specific manifolds, or rely on optimization problems that are difficult to solve, especially when it comes to dictionary learning.

Dictionary Learning

A Framework for Shape Analysis via Hilbert Space Embedding

no code implementations13 Dec 2014 Sadeep Jayasumana, Mathieu Salzmann, Hongdong Li, Mehrtash Harandi

We propose a framework for 2D shape analysis using positive definite kernels defined on Kendall's shape manifold.

Clustering General Classification +1

Optimizing Over Radial Kernels on Compact Manifolds

no code implementations CVPR 2014 Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann, Hongdong Li, Mehrtash Harandi

We tackle the problem of optimizing over all possible positive definite radial kernels on Riemannian manifolds for classification.

General Classification

Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices

no code implementations CVPR 2013 Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann, Hongdong Li, Mehrtash Harandi

To encode the geometry of the manifold in the mapping, we introduce a family of provably positive definite kernels on the Riemannian manifold of SPD matrices.

Motion Segmentation Pedestrian Detection +2

Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels

no code implementations30 Nov 2014 Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann, Hongdong Li, Mehrtash Harandi

We then use the proposed framework to identify positive definite kernels on two specific manifolds commonly encountered in computer vision: the Riemannian manifold of symmetric positive definite matrices and the Grassmann manifold, i. e., the Riemannian manifold of linear subspaces of a Euclidean space.

Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences

no code implementations30 Aug 2014 Mehrtash Harandi, Richard Hartley, Brian Lovell, Conrad Sanderson

This paper introduces sparse coding and dictionary learning for Symmetric Positive Definite (SPD) matrices, which are often used in machine learning, computer vision and related areas.

Action Recognition Dictionary Learning +4

Kernel Coding: General Formulation and Special Cases

no code implementations30 Aug 2014 Mehrtash Harandi, Mathieu Salzmann

In contrast, here, we study the problem of performing coding in a high-dimensional Hilbert space, where the classes are expected to be more easily separable.

Dictionary Learning

Multi-Shot Person Re-Identification via Relational Stein Divergence

no code implementations4 Mar 2014 Azadeh Alavi, Yan Yang, Mehrtash Harandi, Conrad Sanderson

The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately.

General Classification Person Re-Identification

Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds

no code implementations31 Jan 2014 Mehrtash Harandi, Richard Hartley, Chunhua Shen, Brian Lovell, Conrad Sanderson

With the aim of building a bridge between the two realms, we address the problem of sparse coding and dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds.

Action Recognition Classification +6

Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution

no code implementations18 Oct 2013 Mehrtash Harandi, Conrad Sanderson, Chunhua Shen, Brian C. Lovell

Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry.

Action Recognition Dictionary Learning +5

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