no code implementations • 27 Mar 2024 • Yanshuo Wang, Ali Cheraghian, Zeeshan Hayder, Jie Hong, Sameera Ramasinghe, Shafin Rahman, David Ahmedt-Aristizabal, Xuesong Li, Lars Petersson, Mehrtash Harandi
Here, we propose a novel method that uses a backpropagation-free approach for TTA for the specific case of 3D data.
no code implementations • 26 Jan 2024 • Yan Yang, Md Zakir Hossain, Xuesong Li, Shafin Rahman, Eric Stone
Spatial transcriptomics (ST) captures gene expression within distinct regions (i. e., windows) of a tissue slide.
1 code implementation • 18 Oct 2023 • Fahimul Hoque Shubho, Townim Faisal Chowdhury, Ali Cheraghian, Morteza Saberi, Nabeel Mohammed, Shafin Rahman
Then, we enrich word vectors by combining the word embeddings from class names and descriptions generated by ChatGPT.
no code implementations • 5 Oct 2023 • Yanshuo Wang, Jie Hong, Ali Cheraghian, Shafin Rahman, David Ahmedt-Aristizabal, Lars Petersson, Mehrtash Harandi
DSS consists of dynamic thresholding, positive learning, and negative learning processes.
1 code implementation • 5 Oct 2023 • Md. Ismail Hossain, M M Lutfe Elahi, Sameera Ramasinghe, Ali Cheraghian, Fuad Rahman, Nabeel Mohammed, Shafin Rahman
In knowledge distillation literature, feature-based methods have dominated due to their ability to effectively tap into extensive teacher models.
Ranked #1 on Classification on CIFAR-100
1 code implementation • 27 Apr 2023 • H M Dipu Kabir, Subrota Kumar Mondal, Sadia Khanam, Abbas Khosravi, Shafin Rahman, Mohammad Reza Chalak Qazani, Roohallah Alizadehsani, Houshyar Asadi, Shady Mohamed, Saeid Nahavandi, U Rajendra Acharya
In the proposed NN training method for UQ, first, we train a shallow NN for the point prediction.
1 code implementation • 24 Dec 2022 • Md. Ismail Hossain, Mohammed Rakib, M. M. Lutfe Elahi, Nabeel Mohammed, Shafin Rahman
This research aims to generate winning lottery tickets from a set of lottery tickets that can achieve similar accuracy to the original unpruned network.
1 code implementation • 30 Oct 2022 • Yan Yang, Md Zakir Hossain, Eric A Stone, Shafin Rahman
This paper proposes an Exemplar Guided Network (EGN) to accurately and efficiently predict gene expression directly from each window of a tissue slide image.
1 code implementation • 9 Oct 2022 • Md. Tahrim Faroque, Yan Yang, Md Zakir Hossain, Sheikh Motahar Naim, Nabeel Mohammed, Shafin Rahman
Smile veracity classification is a task of interpreting social interactions.
no code implementations • 29 Sep 2022 • Majid Nasiri, Ali Cheraghian, Townim Faisal Chowdhury, Sahar Ahmadi, Morteza Saberi, Shafin Rahman
To address this problem, we propose a prompt-guided 3D scene generation and supervision method that augments 3D data to learn the network better, exploring the complex interplay of seen and unseen objects.
1 code implementation • 30 May 2022 • Townim Chowdhury, Ali Cheraghian, Sameera Ramasinghe, Sahar Ahmadi, Morteza Saberi, Shafin Rahman
Few-shot class-incremental learning (FSCIL) aims to incrementally fine-tune a model (trained on base classes) for a novel set of classes using a few examples without forgetting the previous training.
no code implementations • 23 May 2022 • Md Sazzad Hossain, Pritom Saha, Townim Faisal Chowdhury, Shafin Rahman, Fuad Rahman, Nabeel Mohammed
A common goal of task-incremental methods is to design a network that can operate on minimal size, maintaining decent performance.
1 code implementation • 23 Mar 2022 • Yan Yang, Zakir Hossain, Khandaker Asif, Liyuan Pan, Shafin Rahman, Eric Stone
De novo peptide sequencing aims to recover amino acid sequences of a peptide from tandem mass spectrometry (MS) data.
1 code implementation • 27 Jun 2021 • Townim Chowdhury, Mahira Jalisha, Ali Cheraghian, Shafin Rahman
Experimenting on three 3D point cloud recognition backbones (PointNet, DGCNN, and PointConv) and synthetic (ModelNet40, ModelNet10) and real scanned (ScanObjectNN) datasets, we establish new baseline results on learning without forgetting for 3D data.
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.
no code implementations • ICCV 2021 • Ali Cheraghian, Shafin Rahman, Sameera Ramasinghe, Pengfei Fang, Christian Simon, Lars Petersson, Mehrtash Harandi
In this paper, we propose addressing this problem using a mixture of subspaces.
1 code implementation • 30 Nov 2020 • Yan Yang, Md Zakir Hossain, Tom Gedeon, Shafin Rahman
Instead of constraining the translation process by using a reference image, the users can command the model to retouch the generated images by involving the semantic information in the generation process.
1 code implementation • 24 Oct 2020 • Shafin Rahman, Sejuti Rahman, Omar Shahid, Md. Tahmeed Abdullah, Jubair Ahmed Sourov
A plethora of research in the literature shows how human eye fixation pattern varies depending on different factors, including genetics, age, social functioning, cognitive functioning, and so on.
2 code implementations • 19 Oct 2020 • Nasir Hayat, Munawar Hayat, Shafin Rahman, Salman Khan, Syed Waqas Zamir, Fahad Shahbaz Khan
The existing zero-shot detection approaches project visual features to the semantic domain for seen objects, hoping to map unseen objects to their corresponding semantics during inference.
Ranked #1 on Zero-Shot Object Detection on ImageNet Detection
no code implementations • 7 Oct 2020 • Yan Yang, Md Zakir Hossain, Tom Gedeon, Shafin Rahman
Smiles play a vital role in the understanding of social interactions within different communities, and reveal the physical state of mind of people in both real and deceptive ways.
no code implementations • 16 Mar 2020 • Shafin Rahman, Salman Khan, Nick Barnes, Fahad Shahbaz Khan
Any-shot detection offers unique challenges compared to conventional novel object detection such as, a high imbalance between unseen, few-shot and seen object classes, susceptibility to forget base-training while learning novel classes and distinguishing novel classes from the background.
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.
no code implementations • ICCV 2019 • Shafin Rahman, Salman Khan, Nick Barnes
To the best of our knowledge, we are the first to propose a transductive zero-shot object detection approach that convincingly reduces the domain-shift and model-bias against unseen classes.
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.
1 code implementation • 27 Feb 2019 • Ali Cheraghian, Shafin Rahman, Lars Petersson
A challenge for a 3D point cloud recognition system is, then, to classify objects from new, unseen, classes.
3 code implementations • 22 Nov 2018 • Shafin Rahman, Salman Khan, Nick Barnes
This setting gives rise to the need for correct alignment between visual and semantic concepts, so that the unseen objects can be identified using only their semantic attributes.
Ranked #5 on Zero-Shot Object Detection on PASCAL VOC'07
1 code implementation • 22 Nov 2018 • Muzammal Naseer, Salman H. Khan, Shafin Rahman, Fatih Porikli
Deep neural networks (DNNs) can be easily fooled by adding human imperceptible perturbations to the images.
1 code implementation • 16 Mar 2018 • Shafin Rahman, Salman Khan, Fatih Porikli
We hypothesize that this setting is ill-suited for real-world applications where unseen objects appear only as a part of a complex scene, warranting both the `recognition' and `localization' of an unseen category.
1 code implementation • 16 Mar 2018 • Shafin Rahman, Salman Khan
In-line with the success of deep learning on traditional recognition problem, several end-to-end deep models for zero-shot recognition have been proposed in the literature.
no code implementations • 27 Jun 2017 • Shafin Rahman, Salman H. Khan, Fatih Porikli
Then, it learns how to combine these directions to obtain the principal direction for each unseen class such that the CAPD of the test image is aligned with the semantic embedding of the true class, and opposite to the other classes.
no code implementations • NeurIPS 2015 • Shafin Rahman, Neil Bruce
In this paper we present a definition for visual saliency grounded in information theory.