70 citations · 204 across the 18 of their papers we have counts for
19 papers
Video-R2: Reinforcing Consistent and Grounded Reasoning in Multimodal Language Models
Muhammad Maaz, Hanoona Rasheed, Fahad Shahbaz Khan +1
Reasoning over dynamic visual content remains a central challenge for multimodal large language models. Recent thinking models generate explicit reasoning traces for interpretabili…
Video-CoM: Interactive Video Reasoning via Chain of Manipulations
Hanoona Rasheed, Mohammed Zumri, Muhammad Maaz +3
Recent multimodal large language models (MLLMs) have advanced video understanding, yet most still "think about videos" ie once a video is encoded, reasoning unfolds entirely in tex…
VideoMathQA: Benchmarking Mathematical Reasoning via Multimodal Understanding in Videos
Hanoona Rasheed, Abdelrahman Shaker, Anqi Tang +4
Mathematical reasoning in real-world video settings presents a fundamentally different challenge than in static images or text. It requires interpreting fine-grained visual informa…
A Culturally-diverse Multilingual Multimodal Video Benchmark & Model
Bhuiyan Sanjid Shafique, Ashmal Vayani, Muhammad Maaz +26
Large multimodal models (LMMs) have recently gained attention due to their effectiveness to understand and generate descriptions of visual content. Most existing LMMs are in Englis…
PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding
Jang Hyun Cho, Andrea Madotto, Effrosyni Mavroudi +26
Vision-language models are integral to computer vision research, yet many high-performing models remain closed-source, obscuring their data, design and training recipe. The researc…
Mobile-VideoGPT: Fast and Accurate Model for Mobile Video Understanding
Abdelrahman Shaker, Muhammad Maaz, Chenhui Gou +3
Video understanding models often struggle with high computational requirements, extensive parameter counts, and slow inference speed, making them inefficient for practical use. To…
VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding
Muhammad Maaz, Hanoona Rasheed, Salman Khan +1
Building on the advances of language models, Large Multimodal Models (LMMs) have contributed significant improvements in video understanding. While the current video LMMs utilize a…
PALO: A Polyglot Large Multimodal Model for 5B People
Muhammad Maaz, Hanoona Rasheed, Abdelrahman Shaker +6
In pursuit of more inclusive Vision-Language Models (VLMs), this study introduces a Large Multilingual Multimodal Model called PALO. PALO offers visual reasoning capabilities in 10…
PG-Video-LLaVA: Pixel Grounding Large Video-Language Models
Shehan Munasinghe, Rusiru Thushara, Muhammad Maaz +4
Extending image-based Large Multimodal Models (LMMs) to videos is challenging due to the inherent complexity of video data. The recent approaches extending image-based LMMs to vide…
GLaMM: Pixel Grounding Large Multimodal Model
Hanoona Rasheed, Muhammad Maaz, Sahal Shaji Mullappilly +7
Large Multimodal Models (LMMs) extend Large Language Models to the vision domain. Initial LMMs used holistic images and text prompts to generate ungrounded textual responses. Recen…
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
Muhammad Maaz, Hanoona Rasheed, Salman Khan +1
Conversation agents fueled by Large Language Models (LLMs) are providing a new way to interact with visual data. While there have been initial attempts for image-based conversation…
SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications
Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed +3
Self-attention has become a defacto choice for capturing global context in various vision applications. However, its quadratic computational complexity with respect to image resolu…
UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation
Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed +3
Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks. Within the transformer models, the self-attention mechanism is…
Fine-tuned CLIP Models are Efficient Video Learners
Hanoona Rasheed, Muhammad Uzair Khattak, Muhammad Maaz +2
Large-scale multi-modal training with image-text pairs imparts strong generalization to CLIP model. Since training on a similar scale for videos is infeasible, recent approaches fo…
MaPLe: Multi-modal Prompt Learning
Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz +2
Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks. However, they are sensitive to the choice of input text promp…
Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection
Hanoona Rasheed, Muhammad Maaz, Muhammad Uzair Khattak +2
Existing open-vocabulary object detectors typically enlarge their vocabulary sizes by leveraging different forms of weak supervision. This helps generalize to novel objects at infe…
EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications
Muhammad Maaz, Abdelrahman Shaker, Hisham Cholakkal +4
In the pursuit of achieving ever-increasing accuracy, large and complex neural networks are usually developed. Such models demand high computational resources and therefore cannot…
Class-agnostic Object Detection with Multi-modal Transformer
Muhammad Maaz, Hanoona Rasheed, Salman Khan +3
What constitutes an object? This has been a long-standing question in computer vision. Towards this goal, numerous learning-free and learning-based approaches have been developed t…
Self-Supervised Learning for Fine-Grained Visual Categorization
Muhammad Maaz, Hanoona Abdul Rasheed, Dhanalaxmi Gaddam
Recent research in self-supervised learning (SSL) has shown its capability in learning useful semantic representations from images for classification tasks. Through our work, we st…