2 citations · 2 across the 9 of their papers we have counts for
13 papers
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…
Triangular Consistency as a Universal Constraint for Learning Optical Flow
Yi Xiao, Carlos Rodriguez Coronel, Jing Zhan +3
We propose triangular consistency as a first-principled constraint for optical flow, which is agnostic to network architecture, supervision type, and dataset, and applies to both i…
Can MLLMs Critique Like Humans? Evaluating Open-Ended Aesthetic Reasoning in Multimodal Large Language Models
Sajjad Ghiasvand, Maryam Amirizaniani, Haniyeh Ehsani Oskouie +2
Open-ended aesthetic critique is a challenge for multimodal large language models (MLLMs): it has no single correct answer, and most aesthetic evaluation measures models against nu…
SciPredict: Can LLMs Predict the Outcomes of Scientific Experiments in Natural Sciences?
Udari Madhushani Sehwag, Elaine Lau, Haniyeh Ehsani Oskouie +14
Accelerating scientific discovery requires the identification of which experiments would yield the best outcomes before committing resources to costly physical validation. While ex…
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…
MI-to-Mid Distilled Compression (M2M-DC): An Hybrid-Information-Guided-Block Pruning with Progressive Inner Slicing Approach to Model Compression
Lionel Levine, Haniyeh Ehsani Oskouie, Sajjad Ghiasvand +1
We introduce MI-to-Mid Distilled Compression (M2M-DC), a two-scale, shape-safe compression framework that interleaves information-guided block pruning with progressive inner slicin…
Test-Time Defense Against Adversarial Attacks via Stochastic Resonance of Latent Ensembles
Dong Lao, Yuxiang Zhang, Haniyeh Ehsani Oskouie +3
We propose a test-time defense mechanism against adversarial attacks: imperceptible image perturbations that significantly alter the predictions of a model. Unlike existing methods…
Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models
Sajjad Ghiasvand, Haniyeh Ehsani Oskouie, Mahnoosh Alizadeh +1
Vision-Language Models (VLMs) such as CLIP have shown remarkable performance in cross-modal tasks through large-scale contrastive pre-training. To adapt these large transformer-bas…
Exploring the Impact of Dataset Statistical Effect Size on Model Performance and Data Sample Size Sufficiency
Arya Hatamian, Lionel Levine, Haniyeh Ehsani Oskouie +1
Having a sufficient quantity of quality data is a critical enabler of training effective machine learning models. Being able to effectively determine the adequacy of a dataset prio…
Leveraging Large Language Models and Topic Modeling for Toxicity Classification
Haniyeh Ehsani Oskouie, Christina Chance, Claire Huang +3
Content moderation and toxicity classification represent critical tasks with significant social implications. However, studies have shown that major classification models exhibit t…
Exploring Cross-model Neuronal Correlations in the Context of Predicting Model Performance and Generalizability
Haniyeh Ehsani Oskouie, Sajjad Ghiasvand, Lionel Levine +1
As Artificial Intelligence (AI) models are increasingly integrated into critical systems, the need for a robust framework to establish the trustworthiness of AI is increasingly par…
Attack on Scene Flow using Point Clouds
Haniyeh Ehsani Oskouie, Mohammad-Shahram Moin, Shohreh Kasaei
Deep neural networks have made significant advancements in accurately estimating scene flow using point clouds, which is vital for many applications like video analysis, action rec…
Interpretation of Neural Networks is Susceptible to Universal Adversarial Perturbations
Haniyeh Ehsani Oskouie, Farzan Farnia
Interpreting neural network classifiers using gradient-based saliency maps has been extensively studied in the deep learning literature. While the existing algorithms manage to ach…