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Ali Cheraghian

8 accepted papers

2026

Adapt-As-You-Walk Through the Clouds: Training-Free Online Test-Time Adaptation of 3D Vision-Language Foundation Models

Mehran Tamjidi, Hamidreza Dastmalchi, Mohammadreza Alimoradijazi, Ali Cheraghian, Aijun An, Morteza Saberi
AAAI 2026technical

3D Vision-Language Foundation Models (VLFMs) have demonstrated strong generalization and zero-shot recognition capabilities in open-world point cloud processing tasks. However, their performance often degrades in practical scenarios where data are noisy, incomplete, or drawn from distributions that

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2026

DTO-KD: Dynamic Trade-off Optimization for Effective Knowledge Distillation

Zeeshan Hayder, Ali Cheraghian, Lars Petersson, Mehrtash Harandi, Richard Hartley
ICLR 2026oral

Knowledge Distillation (KD) is a widely adopted framework for compressing large models into compact student models by transferring knowledge from a high-capacity teacher. Despite its success, KD presents two persistent challenges: (1) the trade-off between optimizing for the primary task loss and mi…

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2026

Fighting Hallucinations with Counterfactuals: Diffusion-Guided Perturbations for LVLM Hallucination Suppression

Hamidreza Dastmalchi, Aijun An, Ali Cheraghian, Hamed Barzamini
CVPR 2026

While large vision-language models (LVLMs) achieve strong performance on multimodal tasks, they frequently generate hallucinations--unfaithful outputs misaligned with the visual input. To address this issue, we introduce CIPHER (Counterfactual Image Perturbations for Hallucination Extraction and Rem

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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…

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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 ↗
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…

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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)…

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