183 citations · 198 across the 25 of their papers we have counts for
26 papers
Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM
Amol Harsh, Zongyan Han, Jean Lahoud +5
Natural-language queries about 3D environments become actionable when responses are verifiable and metric. Verifiability requires explicit grounding to the referred 3D region, whil…
A Benchmark for Omni-Modal Reasoning in Long Videos
Mohammed Irfan Kurpath, Jaseel Muhammad Kaithakkodan, Jinxing Zhou +12
Long-form omni-modal video understanding requires integrating vision, speech, and ambient audio with coherent long-context reasoning. Existing video benchmarks often trade off temp…
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…
How Good are Foundation Models in Step-by-Step Embodied Reasoning?
Dinura Dissanayake, Ahmed Heakl, Omkar Thawakar +9
Embodied agents operating in the physical world must make decisions that are not only effective but also safe, spatially coherent, and grounded in context. While recent advances in…
InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
Daniya Najiha Abdul Kareem, Abdul Hannan, Mubashir Noman +3
Accurate microscopic medical image segmentation plays a crucial role in diagnosing various cancerous cells and identifying tumors. Driven by advancements in deep learning, convolut…
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…
Open-Set Semi-Supervised Learning for Long-Tailed Medical Datasets
Daniya Najiha A. Kareem, Jean Lahoud, Mustansar Fiaz +2
Many practical medical imaging scenarios include categories that are under-represented but still crucial. The relevance of image recognition models to real-world applications lies…
Agent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agentic Tasks
Tajamul Ashraf, Amal Saqib, Hanan Ghani +11
Deep reasoning is fundamental for solving complex tasks, especially in vision-centric scenarios that demand sequential, multimodal understanding. However, existing benchmarks typic…
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…
DriveLMM-o1: A Step-by-Step Reasoning Dataset and Large Multimodal Model for Driving Scenario Understanding
Ayesha Ishaq, Jean Lahoud, Ketan More +10
While large multimodal models (LMMs) have demonstrated strong performance across various Visual Question Answering (VQA) tasks, certain challenges require complex multi-step reason…
LLMVoX: Autoregressive Streaming Text-to-Speech Model for Any LLM
Sambal Shikhar, Mohammed Irfan Kurpath, Sahal Shaji Mullappilly +5
Recent advancements in speech-to-speech dialogue systems leverage LLMs for multimodal interactions, yet they remain hindered by fine-tuning requirements, high computational overhea…
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…
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…
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…
PARIS3D: Reasoning-based 3D Part Segmentation Using Large Multimodal Model
Amrin Kareem, Jean Lahoud, Hisham Cholakkal
Recent advancements in 3D perception systems have significantly improved their ability to perform visual recognition tasks such as segmentation. However, these systems still heavil…
TransRadar: Adaptive-Directional Transformer for Real-Time Multi-View Radar Semantic Segmentation
Yahia Dalbah, Jean Lahoud, Hisham Cholakkal
Scene understanding plays an essential role in enabling autonomous driving and maintaining high standards of performance and safety. To address this task, cameras and laser scanner…
3D Indoor Instance Segmentation in an Open-World
Mohamed El Amine Boudjoghra, Salwa K. Al Khatib, Jean Lahoud +4
Existing 3D instance segmentation methods typically assume that all semantic classes to be segmented would be available during training and only seen categories are segmented at in…
RadarFormer: Lightweight and Accurate Real-Time Radar Object Detection Model
Yahia Dalbah, Jean Lahoud, Hisham Cholakkal
The performance of perception systems developed for autonomous driving vehicles has seen significant improvements over the last few years. This improvement was associated with the…
Surface-biased Multi-Level Context 3D Object Detection
Sultan Abu Ghazal, Jean Lahoud, Rao Anwer
Object detection in 3D point clouds is a crucial task in a range of computer vision applications including robotics, autonomous cars, and augmented reality. This work addresses the…
CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection
Dhanalaxmi Gaddam, Jean Lahoud, Fahad Shahbaz Khan +2
Existing deep learning-based 3D object detectors typically rely on the appearance of individual objects and do not explicitly pay attention to the rich contextual information of th…
3D Vision with Transformers: A Survey
Jean Lahoud, Jiale Cao, Fahad Shahbaz Khan +4
The success of the transformer architecture in natural language processing has recently triggered attention in the computer vision field. The transformer has been used as a replace…
On the Robustness of 3D Object Detectors
Fatima Albreiki, Sultan Abughazal, Jean Lahoud +3
In recent years, significant progress has been achieved for 3D object detection on point clouds thanks to the advances in 3D data collection and deep learning techniques. Neverthel…
RGB-based Semantic Segmentation Using Self-Supervised Depth Pre-Training
Jean Lahoud, Bernard Ghanem
Although well-known large-scale datasets, such as ImageNet, have driven image understanding forward, most of these datasets require extensive manual annotation and are thus not eas…