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Shafin Rahman

9 accepted papers

2026

LumiNet: Perception-Driven Knowledge Distillation via Statistical Logit Calibration

MD ISMAIL HOSSAIN, M M Lutfe Elahi, Sameera Ramasinghe, Ali Cheraghian, Fuad Rahman, Nabeel Mohammed +1
ICML 2026poster

In the knowledge distillation literature, feature-based methods have dominated due to their ability to effectively tap into extensive teacher models. In contrast, logit-based approaches, which aim to distill `dark knowledge' from teachers, typically exhibit inferior performance compared to feature-b…

Cited by 0Source ↗Scholar ↗
2024

Backpropagation-free Network for 3D Test-time Adaptation

Yanshuo Wang, Ali Cheraghian, Zeeshan Hayder, Jie Hong, Sameera Ramasinghe, Shafin Rahman +4
CVPR 2024poster

Real-world systems often encounter new data over time which leads to experiencing target domain shifts. Existing Test-Time Adaptation (TTA) methods tend to apply computationally heavy and memory-intensive backpropagation-based approaches to handle this. Here we propose a novel method that uses a bac…

Cited by 7Source ↗PDF ↗code ↗Scholar ↗
2024

Canonical Shape Projection is All You Need for 3D Few-shot Class Incremental Learning

Ali Cheraghian*, Zeeshan Hayder, Sameeea Ramasinghe, Shafin Rahman, Javad Jafaryahya, Lars Petersson +1
ECCV 2024poster

"In recent years, robust pre-trained foundation models have been successfully used in many downstream tasks. Here, we would like to use such powerful models to address the problem of few-shot class incremental learning (FSCIL) tasks on 3D point cloud objects. Our approach is to reprogram the well-kn…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2024

SEA-GWNN: Simple and Effective Adaptive Graph Wavelet Neural Network

Swakshar Deb, Sejuti Rahman, Shafin Rahman
AAAI 2024technical

The utilization of wavelet-based techniques in graph neural networks (GNNs) has gained considerable attention, particularly in the context of node classification. Although existing wavelet-based approaches have shown promise, they are constrained by their reliance on pre-defined wavelet filters, ren…

Cited by 2Source ↗PDF ↗Scholar ↗
2022

Few-Shot Class-Incremental Learning for 3D Point Cloud Objects

Townim Chowdhury, Ali Cheraghian, Sameera Ramasinghe, Sahar Ahmadi, Morteza Saberi, Shafin Rahman
ECCV 2022poster

"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. Recent efforts of FSCIL addresses this problem primarily on 2D image data. However, due to the advanc…

Cited by 21Source ↗PDF ↗code ↗Scholar ↗
2021

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

Ali Cheraghian, Shafin Rahman, Pengfei Fang, Soumava Kumar Roy, Lars Petersson, Mehrtash Harandi
CVPR 2021poster

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. Due to the limited number of examples for training, the techniques developed for standard incremental learning cannot be applied ve…

Cited by 241PDF ↗Scholar ↗
2021

Synthesized Feature Based Few-Shot Class-Incremental Learning on a Mixture of Subspaces

Ali Cheraghian, Shafin Rahman, Sameera Ramasinghe, Pengfei Fang, Christian Simon, Lars Petersson +1
ICCV 2021poster

Few-shot class incremental learning (FSCIL) aims to incrementally add sets of novel classes to a well-trained base model in multiple training sessions with the restriction that only a few novel instances are available per class. While learning novel classes, FSCIL methods gradually forget base (old)…

Cited by 86PDF ↗Scholar ↗