14 citations · 29 across the 24 of their papers we have counts for
17 papers · 1 filter
UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities
Muhammad Uzair Khattak, Shahina Kunhimon, Muzammal Naseer +2
Vision-Language Models (VLMs) trained via contrastive learning have achieved notable success in natural image tasks. However, their application in the medical domain remains limite…
BAPLe: Backdoor Attacks on Medical Foundational Models using Prompt Learning
Asif Hanif, Fahad Shamshad, Muhammad Awais +5
Medical foundation models are gaining prominence in the medical community for their ability to derive general representations from extensive collections of medical image-text pairs…
VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs
Rohit Bharadwaj, Hanan Gani, Muzammal Naseer +2
The recent developments in Large Multi-modal Video Models (Video-LMMs) have significantly enhanced our ability to interpret and analyze video data. Despite their impressive capabil…
Towards Evaluating the Robustness of Visual State Space Models
Hashmat Shadab Malik, Fahad Shamshad, Muzammal Naseer +3
Vision State Space Models (VSSMs), a novel architecture that combines the strengths of recurrent neural networks and latent variable models, have demonstrated remarkable performanc…
On Evaluating Adversarial Robustness of Volumetric Medical Segmentation Models
Hashmat Shadab Malik, Numan Saeed, Asif Hanif +4
Volumetric medical segmentation models have achieved significant success on organ and tumor-based segmentation tasks in recent years. However, their vulnerability to adversarial at…
How Good is my Video LMM? Complex Video Reasoning and Robustness Evaluation Suite for Video-LMMs
Muhammad Uzair Khattak, Muhammad Ferjad Naeem, Jameel Hassan +4
Recent advancements in Large Language Models (LLMs) have led to the development of Video Large Multi-modal Models (Video-LMMs) that can handle a wide range of video understanding t…
Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot Learning
Wenjin Hou, Shiming Chen, Shuhuang Chen +6
Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for unseen classes, which is an effective way to advance ZSL. However, existing generative metho…
Cross-Modal Self-Training: Aligning Images and Pointclouds to Learn Classification without Labels
Amaya Dharmasiri, Muzammal Naseer, Salman Khan +1
Large-scale vision 2D vision language models, such as CLIP can be aligned with a 3D encoder to learn generalizable (open-vocabulary) 3D vision models. However, current methods requ…
Language Guided Domain Generalized Medical Image Segmentation
Shahina Kunhimon, Muzammal Naseer, Salman Khan +1
Single source domain generalization (SDG) holds promise for more reliable and consistent image segmentation across real-world clinical settings particularly in the medical domain,…
Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image Restoration
Akshay Dudhane, Omkar Thawakar, Syed Waqas Zamir +3
All-in-one image restoration tackles different types of degradations with a unified model instead of having task-specific, non-generic models for each degradation. The requirement…
Composed Video Retrieval via Enriched Context and Discriminative Embeddings
Omkar Thawakar, Muzammal Naseer, Rao Muhammad Anwer +4
Composed video retrieval (CoVR) is a challenging problem in computer vision which has recently highlighted the integration of modification text with visual queries for more sophist…
Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation Learning
Hasindri Watawana, Kanchana Ranasinghe, Tariq Mahmood +3
Self-supervised representation learning has been highly promising for histopathology image analysis with numerous approaches leveraging their patient-slide-patch hierarchy to learn…
Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery
Mubashir Noman, Muzammal Naseer, Hisham Cholakkal +3
Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks by pre-training on large amount of un…
VURF: A General-purpose Reasoning and Self-refinement Framework for Video Understanding
Ahmad Mahmood, Ashmal Vayani, Muzammal Naseer +2
Recent studies have demonstrated the effectiveness of Large Language Models (LLMs) as reasoning modules that can deconstruct complex tasks into more manageable sub-tasks, particula…
ObjectCompose: Evaluating Resilience of Vision-Based Models on Object-to-Background Compositional Changes
Hashmat Shadab Malik, Muhammad Huzaifa, Muzammal Naseer +2
Given the large-scale multi-modal training of recent vision-based models and their generalization capabilities, understanding the extent of their robustness is critical for their r…
MedContext: Learning Contextual Cues for Efficient Volumetric Medical Segmentation
Hanan Gani, Muzammal Naseer, Fahad Khan +1
Volumetric medical segmentation is a critical component of 3D medical image analysis that delineates different semantic regions. Deep neural networks have significantly improved vo…
Video-GroundingDINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding
Syed Talal Wasim, Muzammal Naseer, Salman Khan +2
Video grounding aims to localize a spatio-temporal section in a video corresponding to an input text query. This paper addresses a critical limitation in current video grounding me…