Mehrtash Harandi

research

∙ 08/24/2023

Hyperbolic Audio-visual Zero-shot Learning

Audio-visual zero-shot learning aims to classify samples consisting of a...

0 Jie Hong, et al. ∙

research

∙ 07/31/2023

L3DMC: Lifelong Learning using Distillation via Mixed-Curvature Space

The performance of a lifelong learning (L3) model degrades when it is tr...

0 Kaushik Roy, et al. ∙

research

∙ 07/31/2023

Subspace Distillation for Continual Learning

An ultimate objective in continual learning is to preserve knowledge lea...

0 Kaushik Roy, et al. ∙

research

∙ 07/21/2023

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

Reconstructing soft tissues from stereo endoscope videos is an essential...

0 Ruyi Zha, et al. ∙

research

∙ 06/01/2023

Contrastive Learning MRI Reconstruction

Purpose: We propose a novel contrastive learning latent space representa...

0 Mevan Ekanayake, et al. ∙

research

∙ 04/21/2023

Hyperbolic Geometry in Computer Vision: A Survey

Hyperbolic geometry, a Riemannian manifold endowed with constant section...

0 Pengfei Fang, et al. ∙

research

∙ 02/12/2023

Vector Quantized Wasserstein Auto-Encoder

Learning deep discrete latent presentations offers a promise of better s...

0 Tung-Long Vuong, et al. ∙

research

∙ 12/05/2022

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

Point cloud analysis is receiving increasing attention, however, most ex...

0 Jie Hong, et al. ∙

research

∙ 11/22/2022

Multimorbidity Content-Based Medical Image Retrieval Using Proxies

Content-based medical image retrieval is an important diagnostic tool th...

0 Yunyan Xing, et al. ∙

research

∙ 10/09/2022

A Differentiable Distance Approximation for Fairer Image Classification

Naively trained AI models can be heavily biased. This can be particularl...

0 Nicholas Rosa, et al. ∙

research

∙ 09/16/2022

Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation

As intensities of MRI volumes are inconsistent across institutes, it is ...

0 Himashi Peiris, et al. ∙

research

∙ 09/14/2022

Learning Deep Optimal Embeddings with Sinkhorn Divergences

Deep Metric Learning algorithms aim to learn an efficient embedding spac...

0 Soumava Kumar Roy, et al. ∙

research

∙ 09/13/2022

Defense against Privacy Leakage in Federated Learning

Federated Learning (FL) provides a promising distributed learning paradi...

0 Jing Wu, et al. ∙

research

∙ 08/02/2022

Curved Geometric Networks for Visual Anomaly Recognition

Learning a latent embedding to understand the underlying nature of data ...

0 Jie Hong, et al. ∙

research

∙ 07/25/2022

Deep Laparoscopic Stereo Matching with Transformers

The self-attention mechanism, successfully employed with the transformer...

0 Xuelian Cheng, et al. ∙

research

∙ 07/18/2022

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

Global correlations are widely seen in human anatomical structures due t...

0 Mevan Ekanayake, et al. ∙

research

∙ 06/15/2022

Rethinking Generalization in Few-Shot Classification

Single image-level annotations only correctly describe an often small su...

0 Markus Hiller, et al. ∙

research

∙ 06/15/2022

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

Inspired by the concept of preconditioning, we propose a novel method to...

0 Markus Hiller, et al. ∙

research

∙ 03/23/2022

GOSS: Towards Generalized Open-set Semantic Segmentation

In this paper, we present and study a new image segmentation task, calle...

0 Jie Hong, et al. ∙

research

∙ 03/14/2022

Implicit Motion Handling for Video Camouflaged Object Detection

We propose a new video camouflaged object detection (VCOD) framework tha...

0 Xuelian Cheng, et al. ∙

research

∙ 02/12/2022

A Data-driven Multi-fidelity Physics-informed Learning Framework for Smart Manufacturing: A Composites Processing Case Study

Despite the successful implementations of physics-informed neural networ...

0 Milad Ramezankhani, et al. ∙

research

∙ 01/11/2022

Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task

This paper proposes an adversarial learning based training approach for ...

9 Himashi Peiris, et al. ∙

research

∙ 12/07/2021

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

Learning and generalizing to novel concepts with few samples (Few-Shot L...

0 Rongkai Ma, et al. ∙

research

∙ 12/03/2021

Adaptive Poincaré Point to Set Distance for Few-Shot Classification

Learning and generalizing from limited examples, i,e, few-shot learning,...

0 Rongkai Ma, et al. ∙

research

∙ 11/26/2021

A Volumetric Transformer for Accurate 3D Tumor Segmentation

This paper presents a Transformer architecture for volumetric medical im...

0 Himashi Peiris, et al. ∙

research

∙ 11/12/2021

Learning Online for Unified Segmentation and Tracking Models

Tracking requires building a discriminative model for the target in the ...

0 Tianyu Zhu, et al. ∙

research

∙ 10/26/2021

Meta-Learning for Multi-Label Few-Shot Classification

Even with the luxury of having abundant data, multi-label classification...

12 Christian Simon, et al. ∙

research

∙ 10/23/2021

Towards a Robust Differentiable Architecture Search under Label Noise

Neural Architecture Search (NAS) is the game changer in designing robust...

1 Christian Simon, et al. ∙

research

∙ 08/25/2021

Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation

Segmentation of images is a long-standing challenge in medical AI. This ...

0 Himashi Peiris, et al. ∙

research

∙ 05/10/2021

Semi-Supervised Metric Learning: A Deep Resurrection

Distance Metric Learning (DML) seeks to learn a discriminative embedding...

0 Ujjal Kr Dutta, et al. ∙

research

∙ 04/22/2021

Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions

Recently, adversarial attack methods have been developed to challenge th...

0 Jing Wu, et al. ∙

research

∙ 04/17/2021

On Learning the Geodesic Path for Incremental Learning

Neural networks notoriously suffer from the problem of catastrophic forg...

17 Christian Simon, et al. ∙

research

∙ 04/13/2021

Learning Log-Determinant Divergences for Positive Definite Matrices

Representations in the form of Symmetric Positive Definite (SPD) matrice...

0 Anoop Cherian, et al. ∙

research

∙ 03/06/2021

Learning to Continually Learn Rapidly from Few and Noisy Data

Neural networks suffer from catastrophic forgetting and are unable to se...

0 Nicholas I-Hsien Kuo, et al. ∙

research

∙ 03/06/2021

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

Few-shot class incremental learning (FSCIL) portrays the problem of lear...

0 Ali Cheraghian, et al. ∙

research

∙ 02/28/2021

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

Deep neural networks are known to be data-driven and label noise can hav...

0 Lie Ju, et al. ∙

research

∙ 12/10/2020

Uncertainty-Aware Deep Calibrated Salient Object Detection

Existing deep neural network based salient object detection (SOD) method...

13 Jing Zhang, et al. ∙

research

∙ 11/02/2020

Set Augmented Triplet Loss for Video Person Re-Identification

Modern video person re-identification (re-ID) machines are often trained...

0 Pengfei Fang, et al. ∙

research

∙ 10/26/2020

Hierarchical Neural Architecture Search for Deep Stereo Matching

To reduce the human efforts in neural network design, Neural Architectur...

0 Xuelian Cheng, et al. ∙

research

∙ 10/07/2020

Channel Recurrent Attention Networks for Video Pedestrian Retrieval

Full attention, which generates an attention value per element of the in...

0 Pengfei Fang, et al. ∙

research

∙ 08/22/2020

Unsupervised Deep Metric Learning via Orthogonality based Probabilistic Loss

Metric learning is an important problem in machine learning. It aims to ...

0 Ujjal Kr Dutta, et al. ∙

research

∙ 07/18/2020

MTL2L: A Context Aware Neural Optimiser

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

0 Nicholas I-Hsien Kuo, et al. ∙

research

∙ 06/17/2020

Cross-Correlated Attention Networks for Person Re-Identification

Deep neural networks need to make robust inference in the presence of oc...

0 Jieming Zhou, et al. ∙

research

∙ 03/23/2020

Bridge the Domain Gap Between Ultra-wide-field and Traditional Fundus Images via Adversarial Domain Adaptation

For decades, advances in retinal imaging technology have enabled effecti...

3 Lie Ju, et al. ∙

research

∙ 02/27/2020

Affinity guided Geometric Semi-Supervised Metric Learning

In this paper, we address the semi-supervised metric learning problem, w...

0 Ujjal Kr Dutta, et al. ∙

research

∙ 12/17/2019

A Probabilistic approach for Learning Embeddings without Supervision

For challenging machine learning problems such as zero-shot learning and...

0 Ujjal Kr Dutta, et al. ∙

research

∙ 04/24/2019

Neural Collaborative Subspace Clustering

We introduce the Neural Collaborative Subspace Clustering, a neural mode...

0 Tong Zhang, et al. ∙

research

∙ 11/02/2018

Scalable Deep k-Subspace Clustering

Subspace clustering algorithms are notorious for their scalability issue...

0 Tong Zhang, et al. ∙

research

∙ 04/16/2018

Block Mean Approximation for Efficient Second Order Optimization

Advanced optimization algorithms such as Newton method and AdaGrad benef...

0 Yao Lu, et al. ∙

research

∙ 03/29/2018

Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective

The success of current deep saliency detection methods heavily depends o...

0 Jing Zhang, et al. ∙

Mehrtash Harandi

Featured Co-authors