10 papers
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…
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…
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): unlike multiple-choice aesthetic benchmarks, it has no single correct answer, and most ae…
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…
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…
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…