7 papers
From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models
Kevin Cannons, Saeed Ranjbar Alvar, Mohammad Asiful Hossain +6
Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of…
CPPO: Contrastive Perception Policy Optimization for VLM Agents
Ahmad Rezaei, Mohsen Gholami, Saeed Ranjbar Alvar +5
We introduce CPPO, a Contrastive Perception Policy Optimization method for finetuning vision--language models (VLMs). Reliable perception is a core requirement for VLM-based agents…
Spatial Reasoning with Vision-Language Models in Ego-Centric Multi-View Scenes
Mohsen Gholami, Ahmad Rezaei, Zhou Weimin +4
Understanding 3D spatial relationships remains a major limitation of current Vision-Language Models (VLMs). Prior work has addressed this issue by creating spatial question-answeri…
LaWa: Using Latent Space for In-Generation Image Watermarking
Ahmad Rezaei, Mohammad Akbari, Saeed Ranjbar Alvar +2
With generative models producing high quality images that are indistinguishable from real ones, there is growing concern regarding the malicious usage of AI-generated images. Imper…
DivPrune: Diversity-based Visual Token Pruning for Large Multimodal Models
Saeed Ranjbar Alvar, Gursimran Singh, Mohammad Akbari +1
Large Multimodal Models (LMMs) have emerged as powerful models capable of understanding various data modalities, including text, images, and videos. LMMs encode both text and visua…
CASP: Compression of Large Multimodal Models Based on Attention Sparsity
Mohsen Gholami, Mohammad Akbari, Kevin Cannons +1
In this work, we propose an extreme compression technique for Large Multimodal Models (LMMs). While previous studies have explored quantization as an efficient post-training compre…