7 papers
Exploring the Limits of Pruning: Task-Specific Neurons, Model Collapse, and Recovery in Task-Specific Large Language Models
M. K. Khalidi Siam, Md. Tausif-Ul-Islam, Md. Reshad Romim Khan +5
Neuron pruning is widely used to reduce the computational cost and parameter footprint of large language models, yet it remains unclear whether neurons in task-specific models cont…
PixFoundation: Are We Heading in the Right Direction with Pixel-level Vision Foundation Models?
Mennatullah Siam
Multiple works have emerged to push the boundaries of multi-modal large language models (MLLMs) towards pixel-level understanding. The current trend is to train MLLMs with pixel-le…
PixFoundation 2.0: Do Video Multi-Modal LLMs Use Motion in Visual Grounding?
Mennatullah Siam
Multi-modal large language models (MLLMs) have shown impressive generalization across tasks using images and text modalities. While their extension to video has enabled tasks such…
Multiscale Video Transformers for Class Agnostic Segmentation in Autonomous Driving
Leila Cheshmi, Mennatullah Siam
Ensuring safety in autonomous driving is a complex challenge requiring handling unknown objects and unforeseen driving scenarios. We develop multiscale video transformers capable o…
A Vision Centric Remote Sensing Benchmark
Abduljaleel Adejumo, Faegheh Yeganli, Clifford Broni-bediako +3
Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks but their remote sensing (RS) counterpart are relatively under explored. Unlike n…
The Power of One: A Single Example is All it Takes for Segmentation in VLMs
Mir Rayat Imtiaz Hossain, Mennatullah Siam, Leonid Sigal +1
Large-scale vision-language models (VLMs), trained on extensive datasets of image-text pairs, exhibit strong multimodal understanding capabilities by implicitly learning associatio…