15 citations · 35 across the 22 of their papers we have counts for
21 papers · 1 filter
Open ultrasound foundation model for robust segmentation and clinical measurement across heterogeneous settings
Chao Qin, Fahad Shahbaz Khan, Salman Khan +4
Ultrasound is the most widely deployed imaging modality worldwide, yet clinical AI remains fragmented into narrow single-task models that fail when device, operator, or anatomy cha…
Gaussian-JEPA: Joint-Embedding Predictive Learning for 3D Gaussian Splats
Bin Ren, Qi Ma, Yue Li +7
3D Gaussian Splatting (3DGS) represents 3D content with anisotropic primitives that jointly encode geometry and appearance. Fixed-budget encoders consume sampled observations of Ga…
MAviS: A Multimodal Conversational Assistant For Avian Species
Yevheniia Kryklyvets, Mohammed Irfan Kurpath, Sahal Shaji Mullappilly +5
Fine-grained understanding and species-specific multimodal question answering are vital for advancing biodiversity conservation and ecological monitoring. However, existing multimo…
MediX-R1: Open Ended Medical Reinforcement Learning
Sahal Shaji Mullappilly, Mohammed Irfan Kurpath, Omair Mohamed +5
We introduce MediX-R1, an open-ended Reinforcement Learning (RL) framework for medical multimodal large language models (MLLMs) that enables clinically grounded, free-form answers…
MedROV: Towards Real-Time Open-Vocabulary Detection Across Diverse Medical Imaging Modalities
Tooba Tehreem Sheikh, Jean Lahoud, Rao Muhammad Anwer +3
Traditional object detection models in medical imaging operate within a closed-set paradigm, limiting their ability to detect objects of novel labels. Open-vocabulary object detect…
EvoLMM: Self-Evolving Large Multimodal Models with Continuous Rewards
Omkar Thawakar, Shravan Venkatraman, Ritesh Thawkar +5
Recent advances in large multimodal models (LMMs) have enabled impressive reasoning and perception abilities, yet most existing training pipelines still depend on human-curated dat…
DEFT: Decompositional Efficient Fine-Tuning for Text-to-Image Models
Komal Kumar, Rao Muhammad Anwer, Fahad Shahbaz Khan +3
Efficient fine-tuning of pre-trained Text-to-Image (T2I) models involves adjusting the model to suit a particular task or dataset while minimizing computational resources and limit…
AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock
Umair Nawaz, Muhammad Zaigham Zaheer, Ufaq Khan +4
Crops, fisheries and livestock form the backbone of global food production, essential to feed the ever-growing global population. However, these sectors face considerable challenge…
TAViS: Text-bridged Audio-Visual Segmentation with Foundation Models
Ziyang Luo, Nian Liu, Xuguang Yang +5
Audio-Visual Segmentation (AVS) faces a fundamental challenge of effectively aligning audio and visual modalities. While recent approaches leverage foundation models to address dat…
Tracking Meets Large Multimodal Models for Driving Scenario Understanding
Ayesha Ishaq, Jean Lahoud, Fahad Shahbaz Khan +3
Large Multimodal Models (LMMs) have recently gained prominence in autonomous driving research, showcasing promising capabilities across various emerging benchmarks. LMMs specifical…
CLIMB-3D: Continual Learning for Imbalanced 3D Instance Segmentation
Vishal Thengane, Jean Lahoud, Hisham Cholakkal +4
While 3D instance segmentation (3DIS) has advanced significantly, most existing methods assume that all object classes are known in advance and uniformly distributed. However, this…
LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs
Omkar Thawakar, Dinura Dissanayake, Ketan More +12
Reasoning is a fundamental capability for solving complex multi-step problems, particularly in visual contexts where sequential step-wise understanding is essential. Existing appro…
BiMediX2: Bio-Medical EXpert LMM for Diverse Medical Modalities
Sahal Shaji Mullappilly, Mohammed Irfan Kurpath, Sara Pieri +8
We introduce BiMediX2, a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model that supports text-based and image-based medical interactions. It enables multi-turn c…
CONDA: Condensed Deep Association Learning for Co-Salient Object Detection
Long Li, Nian Liu, Dingwen Zhang +6
Inter-image association modeling is crucial for co-salient object detection. Despite satisfactory performance, previous methods still have limitations on sufficient inter-image ass…
AgriCLIP: Adapting CLIP for Agriculture and Livestock via Domain-Specialized Cross-Model Alignment
Umair Nawaz, Muhammad Awais, Hanan Gani +4
Capitalizing on vast amount of image-text data, large-scale vision-language pre-training has demonstrated remarkable zero-shot capabilities and has been utilized in several applica…
Open3DTrack: Towards Open-Vocabulary 3D Multi-Object Tracking
Ayesha Ishaq, Mohamed El Amine Boudjoghra, Jean Lahoud +4
3D multi-object tracking plays a critical role in autonomous driving by enabling the real-time monitoring and prediction of multiple objects' movements. Traditional 3D tracking sys…
CDChat: A Large Multimodal Model for Remote Sensing Change Description
Mubashir Noman, Noor Ahsan, Muzammal Naseer +4
Large multimodal models (LMMs) have shown encouraging performance in the natural image domain using visual instruction tuning. However, these LMMs struggle to describe the content…
Efficient 3D-Aware Facial Image Editing via Attribute-Specific Prompt Learning
Amandeep Kumar, Muhammad Awais, Sanath Narayan +3
Drawing upon StyleGAN's expressivity and disentangled latent space, existing 2D approaches employ textual prompting to edit facial images with different attributes. In contrast, 3D…
Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation
Mohamed El Amine Boudjoghra, Angela Dai, Jean Lahoud +4
Recent works on open-vocabulary 3D instance segmentation show strong promise, but at the cost of slow inference speed and high computation requirements. This high computation cost…
Multi-modal Generation via Cross-Modal In-Context Learning
Amandeep Kumar, Muzammal Naseer, Sanath Narayan +3
In this work, we study the problem of generating novel images from complex multimodal prompt sequences. While existing methods achieve promising results for text-to-image generatio…
ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection
Mubashir Noman, Mustansar Fiaz, Hisham Cholakkal +2
Deep learning has shown remarkable success in remote sensing change detection (CD), aiming to identify semantic change regions between co-registered satellite image pairs acquired…