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08/24/2023
Hyperbolic Audio-visual Zero-shot Learning
Audio-visual zero-shot learning aims to classify samples consisting of a...
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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...
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07/31/2023
Subspace Distillation for Continual Learning
An ultimate objective in continual learning is to preserve knowledge lea...
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07/21/2023
EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos
Reconstructing soft tissues from stereo endoscope videos is an essential...
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06/01/2023
Contrastive Learning MRI Reconstruction
Purpose: We propose a novel contrastive learning latent space representa...
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04/21/2023
Hyperbolic Geometry in Computer Vision: A Survey
Hyperbolic geometry, a Riemannian manifold endowed with constant section...
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02/12/2023
Vector Quantized Wasserstein Auto-Encoder
Learning deep discrete latent presentations offers a promise of better s...
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12/05/2022
PointCaM: Cut-and-Mix for Open-Set Point Cloud Analysis
Point cloud analysis is receiving increasing attention, however, most ex...
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11/22/2022
Multimorbidity Content-Based Medical Image Retrieval Using Proxies
Content-based medical image retrieval is an important diagnostic tool th...
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10/09/2022
A Differentiable Distance Approximation for Fairer Image Classification
Naively trained AI models can be heavily biased. This can be particularl...
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09/16/2022
Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation
As intensities of MRI volumes are inconsistent across institutes, it is ...
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09/14/2022
Learning Deep Optimal Embeddings with Sinkhorn Divergences
Deep Metric Learning algorithms aim to learn an efficient embedding spac...
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09/13/2022
Defense against Privacy Leakage in Federated Learning
Federated Learning (FL) provides a promising distributed learning paradi...
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08/02/2022
Curved Geometric Networks for Visual Anomaly Recognition
Learning a latent embedding to understand the underlying nature of data ...
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07/25/2022
Deep Laparoscopic Stereo Matching with Transformers
The self-attention mechanism, successfully employed with the transformer...
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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...
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06/15/2022
Rethinking Generalization in Few-Shot Classification
Single image-level annotations only correctly describe an often small su...
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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...
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03/23/2022
GOSS: Towards Generalized Open-set Semantic Segmentation
In this paper, we present and study a new image segmentation task, calle...
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03/14/2022
Implicit Motion Handling for Video Camouflaged Object Detection
We propose a new video camouflaged object detection (VCOD) framework tha...
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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...
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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 ...
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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...
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12/03/2021
Adaptive Poincaré Point to Set Distance for Few-Shot Classification
Learning and generalizing from limited examples, i,e, few-shot learning,...
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11/26/2021
A Volumetric Transformer for Accurate 3D Tumor Segmentation
This paper presents a Transformer architecture for volumetric medical im...
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11/12/2021
Learning Online for Unified Segmentation and Tracking Models
Tracking requires building a discriminative model for the target in the ...
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10/26/2021
Meta-Learning for Multi-Label Few-Shot Classification
Even with the luxury of having abundant data, multi-label classification...
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10/23/2021
Towards a Robust Differentiable Architecture Search under Label Noise
Neural Architecture Search (NAS) is the game changer in designing robust...
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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 ...
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05/10/2021
Semi-Supervised Metric Learning: A Deep Resurrection
Distance Metric Learning (DML) seeks to learn a discriminative embedding...
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04/22/2021
Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions
Recently, adversarial attack methods have been developed to challenge th...
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04/17/2021
On Learning the Geodesic Path for Incremental Learning
Neural networks notoriously suffer from the problem of catastrophic forg...
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04/13/2021
Learning Log-Determinant Divergences for Positive Definite Matrices
Representations in the form of Symmetric Positive Definite (SPD) matrice...
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03/06/2021
Learning to Continually Learn Rapidly from Few and Noisy Data
Neural networks suffer from catastrophic forgetting and are unable to se...
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03/06/2021
Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning
Few-shot class incremental learning (FSCIL) portrays the problem of lear...
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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...
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12/10/2020
Uncertainty-Aware Deep Calibrated Salient Object Detection
Existing deep neural network based salient object detection (SOD) method...
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11/02/2020
Set Augmented Triplet Loss for Video Person Re-Identification
Modern video person re-identification (re-ID) machines are often trained...
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10/26/2020
Hierarchical Neural Architecture Search for Deep Stereo Matching
To reduce the human efforts in neural network design, Neural Architectur...
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10/07/2020
Channel Recurrent Attention Networks for Video Pedestrian Retrieval
Full attention, which generates an attention value per element of the in...
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08/22/2020
Unsupervised Deep Metric Learning via Orthogonality based Probabilistic Loss
Metric learning is an important problem in machine learning. It aims to ...
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07/18/2020
MTL2L: A Context Aware Neural Optimiser
Learning to learn (L2L) trains a meta-learner to assist the learning of ...
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06/17/2020
Cross-Correlated Attention Networks for Person Re-Identification
Deep neural networks need to make robust inference in the presence of oc...
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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...
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02/27/2020
Affinity guided Geometric Semi-Supervised Metric Learning
In this paper, we address the semi-supervised metric learning problem, w...
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12/17/2019
A Probabilistic approach for Learning Embeddings without Supervision
For challenging machine learning problems such as zero-shot learning and...
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04/24/2019
Neural Collaborative Subspace Clustering
We introduce the Neural Collaborative Subspace Clustering, a neural mode...
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11/02/2018
Scalable Deep k-Subspace Clustering
Subspace clustering algorithms are notorious for their scalability issue...
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04/16/2018
Block Mean Approximation for Efficient Second Order Optimization
Advanced optimization algorithms such as Newton method and AdaGrad benef...
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03/29/2018
