21 citations · 25 across the 23 of their papers we have counts for
25 papers
Test-Time Hallucination Control in Large Vision-Language Models
Mehran Tamjidi, Hamidreza Dastmalchi, Ali Cheraghian +3
Object Hallucination in large vision-language models (LVLMs), where models generate non-factual content about input images, remains a critical barrier to their reliability in real-…
Beyond Global Editing: Per-Instance Disentangled Subspaces for Training-Free Hallucination Mitigation in LVLMs
Ali Cheraghian, Hamidreza Dastmalchi, Hamed Barzamini +4
Recent advances in large vision-language models (LVLMs) have enabled powerful multimodal reasoning by integrating visual encoders with large language models (LLMs). However, their…
SteerSeg: Attention Steering for Reasoning Video Segmentation
Ali Cheraghian, Hamidreza Dastmalchi, Abdelwahed Khamis +3
Video reasoning segmentation requires localizing objects across video frames from natural language expressions, often involving spatial reasoning and implicit references. Recent ap…
Fighting Hallucinations with Counterfactuals: Diffusion-Guided Perturbations for LVLM Hallucination Suppression
Hamidreza Dastmalchi, Aijun An, Ali Cheraghian +1
While large vision-language models (LVLMs) achieve strong performance on multimodal tasks, they frequently generate hallucinations -- unfaithful outputs misaligned with the visual…
HIME: Mitigating Object Hallucinations in LVLMs via Hallucination Insensitivity Model Editing
Ahmed Akl, Abdelwahed Khamis, Ali Cheraghian +3
Large Vision-Language Models (LVLMs) have demonstrated impressive multimodal understanding capabilities, yet they remain prone to object hallucination, where models describe non-ex…
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 +3
3D Vision-Language Foundation Models (VLFMs) have shown strong generalization and zero-shot recognition capabilities in open-world point cloud processing tasks. However, these mode…
ETTA: Efficient Test-Time Adaptation for Vision-Language Models through Dynamic Embedding Updates
Hamidreza Dastmalchi, Aijun An, Ali cheraghian
Pretrained vision-language models (VLMs) like CLIP show strong zero-shot performance but struggle with generalization under distribution shifts. Test-Time Adaptation (TTA) addresse…
MoKD: Multi-Task Optimization for Knowledge Distillation
Zeeshan Hayder, Ali Cheraghian, Lars Petersson +1
Compact models can be effectively trained through Knowledge Distillation (KD), a technique that transfers knowledge from larger, high-performing teacher models. Two key challenges…
Test-Time Adaptation of 3D Point Clouds via Denoising Diffusion Models
Hamidreza Dastmalchi, Aijun An, Ali Cheraghian +2
Test-time adaptation (TTA) of 3D point clouds is crucial for mitigating discrepancies between training and testing samples in real-world scenarios, particularly when handling corru…
TPCL: Task Progressive Curriculum Learning for Robust Visual Question Answering
Ahmed Akl, Abdelwahed Khamis, Zhe Wang +3
Visual Question Answering (VQA) systems are notoriously brittle under distribution shifts and data scarcity. While previous solutions-such as ensemble methods and data augmentation…
Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-free Adaptor
Sahar Ahmadi, Ali Cheraghian, Morteza Saberi +4
Recent advances in deep learning for processing point clouds hold increased interest in Few-Shot Class Incremental Learning (FSCIL) for 3D computer vision. This paper introduces a…
3D Point Cloud Network Pruning: When Some Weights Do not Matter
Amrijit Biswas, Md. Ismail Hossain, M M Lutfe Elahi +4
A point cloud is a crucial geometric data structure utilized in numerous applications. The adoption of deep neural networks referred to as Point Cloud Neural Networks (PC- NNs), fo…
Backpropagation-free Network for 3D Test-time Adaptation
Yanshuo Wang, Ali Cheraghian, Zeeshan Hayder +7
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 h…
Continual Test-time Domain Adaptation via Dynamic Sample Selection
Yanshuo Wang, Jie Hong, Ali Cheraghian +4
The objective of Continual Test-time Domain Adaptation (CTDA) is to gradually adapt a pre-trained model to a sequence of target domains without accessing the source data. This pape…
ChatGPT-guided Semantics for Zero-shot Learning
Fahimul Hoque Shubho, Townim Faisal Chowdhury, Ali Cheraghian +3
Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen classes…
LumiNet: Perception-Driven Knowledge Distillation via Statistical Logit Calibration
Md. Ismail Hossain, M M Lutfe Elahi, Sameera Ramasinghe +4
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 appro…
Prompt-guided Scene Generation for 3D Zero-Shot Learning
Majid Nasiri, Ali Cheraghian, Townim Faisal Chowdhury +3
Zero-shot learning on 3D point cloud data is a related underexplored problem compared to its 2D image counterpart. 3D data brings new challenges for ZSL due to the unavailability o…
Few-shot Class-incremental Learning for 3D Point Cloud Objects
Townim Chowdhury, Ali Cheraghian, Sameera Ramasinghe +3
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…
Learning without Forgetting for 3D Point Cloud Objects
Townim Chowdhury, Mahira Jalisha, Ali Cheraghian +1
When we fine-tune a well-trained deep learning model for a new set of classes, the network learns new concepts but gradually forgets the knowledge of old training. In some real-lif…
Zero-Shot Learning on 3D Point Cloud Objects and Beyond
Ali Cheraghian, Shafinn Rahman, Townim F. Chowdhury +2
Zero-shot learning, the task of learning to recognize new classes not seen during training, has received considerable attention in the case of 2D image classification. However, des…
Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning
Ali Cheraghian, Shafin Rahman, Pengfei Fang +3
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…
Transductive Zero-Shot Learning for 3D Point Cloud Classification
Ali Cheraghian, Shafin Rahman, Dylan Campbell +1
Zero-shot learning, the task of learning to recognize new classes not seen during training, has received considerable attention in the case of 2D image classification. However desp…
Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects
Ali Cheraghian, Shafin Rahman, Dylan Campbell +1
The development of advanced 3D sensors has enabled many objects to be captured in the wild at a large scale, and a 3D object recognition system may therefore encounter many objects…
Zero-shot Learning of 3D Point Cloud Objects
Ali Cheraghian, Shafin Rahman, Lars Petersson
Recent deep learning architectures can recognize instances of 3D point cloud objects of previously seen classes quite well. At the same time, current 3D depth camera technology all…