2 citations · 3 across the 10 of their papers we have counts for
5 papers · 1 filter
Kernel-Based Steering of CLIP with Vision-Language Model Preferences
Sajjad Ghiasvand, Haniyeh Ehsani Oskouie, Sina Mansouri +3
Large vision-language models (VLMs) can judge visual similarity, but their judgments are not directly available as compact image embeddings for efficient comparison. We study how t…
Low-Rank Prompt Learning for Vision-Language Models with Fixed-Token Bases
Tanvir Muntakim Tonoy, Sajjad Ghiasvand, Mahnoosh Alizadeh +1
Prompt learning adapts CLIP to downstream recognition by replacing hand-written templates with learned continuous context vectors, which in Context Optimization (CoOp) form a dense…
ZOMP: Zeroth-Order Multi-Modal Prompt Tuning for Vision-Language Models
Sajjad Ghiasvand, Yifan Yang, Mahnoosh Alizadeh +1
Prompt tuning adapts vision--language models with few trainable parameters, but computing their gradients still requires backpropagation through frozen encoders. We introduce ZOMP,…
MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation
Sajjad Ghiasvand, Haniyeh Ehsani Oskouie, Mahnoosh Alizadeh +1
Prompt learning has become a dominant paradigm for adapting vision-language models (VLMs) such as CLIP to downstream tasks without modifying pretrained weights. While extending pro…
pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models
Sajjad Ghiasvand, Mahnoosh Alizadeh, Ramtin Pedarsani
Vision-Language Models (VLMs) like CLIP have demonstrated remarkable generalization in zero- and few-shot settings, but adapting them efficiently to decentralized, heterogeneous da…