Dezhi Luo, Yijiang Li, Maijunxian Wang, Tianwei Zhao, Bingyang Wang, Siheng Wang, Pinyuan Feng, Pooyan Rahmanzadehgervi, Ziqiao Ma, Hokin Deng
arXiv preprint arXiv:2603.07109 2026
We introduce ConservationBench, testing whether VLMs maintain transformation-invariant understanding of physical quantities (number, length, volume, size) across 23,040 questions and 112 models; performance stays near chance and often worsens with real visual content compared to text-only priors.
Dezhi Luo, Yijiang Li, Maijunxian Wang, Tianwei Zhao, Bingyang Wang, Siheng Wang, Pinyuan Feng, Pooyan Rahmanzadehgervi, Ziqiao Ma, Hokin Deng
arXiv preprint arXiv:2603.07109 2026
We introduce ConservationBench, testing whether VLMs maintain transformation-invariant understanding of physical quantities (number, length, volume, size) across 23,040 questions and 112 models; performance stays near chance and often worsens with real visual content compared to text-only priors.
Pooyan Rahmanzadehgervi, Hung Huy Nguyen, Rosanne Liu, Long Mai, Anh Totti Nguyen
IEEE/CVF International Conference on Computer Vision (ICCV) 2025
We propose a 1-head Transformer Attention Bottleneck (TAB) layer inserted after standard self-attention to constrain how much visual information flows through a VLM, enabling interpretable, user-adjustable intervention and debugging, demonstrated on image-difference captioning across three datasets.
Pooyan Rahmanzadehgervi, Hung Huy Nguyen, Rosanne Liu, Long Mai, Anh Totti Nguyen
IEEE/CVF International Conference on Computer Vision (ICCV) 2025
We propose a 1-head Transformer Attention Bottleneck (TAB) layer inserted after standard self-attention to constrain how much visual information flows through a VLM, enabling interpretable, user-adjustable intervention and debugging, demonstrated on image-difference captioning across three datasets.
Pooyan Rahmanzadehgervi, Logan Bolton, Mohammad Reza Taesiri, Anh Totti Nguyen
eXCV Workshop, International Conference on Computer Vision (ICCV) 2025
Pooyan Rahmanzadehgervi, Logan Bolton, Mohammad Reza Taesiri, Anh Totti Nguyen
eXCV Workshop, International Conference on Computer Vision (ICCV) 2025
Hung Huy Nguyen, Pooyan Rahmanzadehgervi, Long Mai, Anh Totti Nguyen
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025
We introduce a change-detection method that leverages correspondences between before/after regions during training and at test time to reduce false positives, improving zero-shot object-level change detection accuracy across in-distribution and cross-domain benchmarks.
Hung Huy Nguyen, Pooyan Rahmanzadehgervi, Long Mai, Anh Totti Nguyen
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025
We introduce a change-detection method that leverages correspondences between before/after regions during training and at test time to reduce false positives, improving zero-shot object-level change detection accuracy across in-distribution and cross-domain benchmarks.
Pooyan Rahmanzadehgervi*, Logan Bolton*, Mohammad Reza Taesiri, Anh Totti Nguyen (* equal contribution)
Asian Conference on Computer Vision (ACCV) 2024 Oral
While VLMs like GPT-4o and Gemini 1.5 Pro score highly on many vision benchmarks, on our BlindTest suite of 7 simple low-level vision tasks (e.g., detecting overlapping circles or counting line intersections), four state-of-the-art VLMs average only 58.07% accuracy, far below human-level performance.
Pooyan Rahmanzadehgervi*, Logan Bolton*, Mohammad Reza Taesiri, Anh Totti Nguyen (* equal contribution)
Asian Conference on Computer Vision (ACCV) 2024 Oral
While VLMs like GPT-4o and Gemini 1.5 Pro score highly on many vision benchmarks, on our BlindTest suite of 7 simple low-level vision tasks (e.g., detecting overlapping circles or counting line intersections), four state-of-the-art VLMs average only 58.07% accuracy, far below human-level performance.
Pooyan Rahmanzadehgervi, Ahad Harati, Sayed Kamaledin Ghiasi-Shirazi
International Conference on Computer Engineering and Knowledge (ICCKE) 2021
We propose a bio-inspired deep reinforcement learning architecture for autonomous obstacle avoidance in drone navigation, learning a generalizable collision-avoidance policy in simulation and comparing it against a human expert baseline.
Pooyan Rahmanzadehgervi, Ahad Harati, Sayed Kamaledin Ghiasi-Shirazi
International Conference on Computer Engineering and Knowledge (ICCKE) 2021
We propose a bio-inspired deep reinforcement learning architecture for autonomous obstacle avoidance in drone navigation, learning a generalizable collision-avoidance policy in simulation and comparing it against a human expert baseline.