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cs.CV2026
LAMP: Learning Universal Adversarial Perturbations for Multi-Image Tasks via Pre-trained Models
Alvi Md Ishmam, Najibul Haque Sarker, Zaber Ibn Abdul Hakim +1
Multimodal Large Language Models (MLLMs) have achieved remarkable performance across vision-language tasks. Recent advancements allow these models to process multiple images as inp…
cs.CV2025
SteerVLM: Robust Model Control through Lightweight Activation Steering for Vision Language Models
Anushka Sivakumar, Andrew Zhang, Zaber Hakim +1
This work introduces SteerVLM, a lightweight steering module designed to guide Vision-Language Models (VLMs) towards outputs that better adhere to desired instructions. Our approac…
cs.CV2025
ENTER: Event Based Interpretable Reasoning for VideoQA
Hammad Ayyubi, Junzhang Liu, Ali Asgarov +10
In this paper, we present ENTER, an interpretable Video Question Answering (VideoQA) system based on event graphs. Event graphs convert videos into graphical representations, where…