33 citations · 133 across the 57 of their papers we have counts for
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Discriminative Image Generation with Diffusion Models for Zero-Shot Learning
Dingjie Fu, Wenjin Hou, Shiming Chen +4
Generative Zero-Shot Learning (ZSL) methods synthesize class-related features based on predefined class semantic prototypes, showcasing superior performance. However, this feature…
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…
EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues
Sagar Soni, Akshay Dudhane, Hiyam Debary +8
Automated analysis of vast Earth observation data via interactive Vision-Language Models (VLMs) can unlock new opportunities for environmental monitoring, disaster response, and {r…
GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks
Muhammad Sohail Danish, Muhammad Akhtar Munir, Syed Roshaan Ali Shah +5
While numerous recent benchmarks focus on evaluating generic Vision-Language Models (VLMs), they do not effectively address the specific challenges of geospatial applications. Gene…
VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos
Shehan Munasinghe, Hanan Gani, Wenqi Zhu +4
Fine-grained alignment between videos and text is challenging due to complex spatial and temporal dynamics in videos. Existing video-based Large Multimodal Models (LMMs) handle bas…
CAMEL-Bench: A Comprehensive Arabic LMM Benchmark
Sara Ghaboura, Ahmed Heakl, Omkar Thawakar +7
Recent years have witnessed a significant interest in developing large multimodal models (LMMs) capable of performing various visual reasoning and understanding tasks. This has led…
How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization?
Jiahua Dong, Wenqi Liang, Hongliu Li +5
Custom diffusion models (CDMs) have attracted widespread attention due to their astonishing generative ability for personalized concepts. However, most existing CDMs unreasonably a…
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…
Frontiers in Intelligent Colonoscopy
Ge-Peng Ji, Jingyi Liu, Peng Xu +4
Colonoscopy is currently one of the most sensitive screening methods for colorectal cancer. This study investigates the frontiers of intelligent colonoscopy techniques and their pr…
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 Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region
Muhammad Akhtar Munir, Fahad Shahbaz Khan, Salman Khan
Accurate weather and climate modeling is critical for both scientific advancement and safeguarding communities against environmental risks. Traditional approaches rely heavily on N…
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…
Connecting Dreams with Visual Brainstorming Instruction
Yasheng Sun, Bohan Li, Mingchen Zhuge +4
Recent breakthroughs in understanding the human brain have revealed its impressive ability to efficiently process and interpret human thoughts, opening up possibilities for interve…
Open-Vocabulary Temporal Action Localization using Multimodal Guidance
Akshita Gupta, Aditya Arora, Sanath Narayan +3
Open-Vocabulary Temporal Action Localization (OVTAL) enables a model to recognize any desired action category in videos without the need to explicitly curate training data for all…
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…
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,…
Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning
Shiming Chen, Wenjin Hou, Salman Khan +1
Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic interactions to transfer semantic knowledge from seen classes to unseen ones, supported by sema…