68 citations · 398 across the 86 of their papers we have counts for
85 papers · 1 filter
Omni-Embed-Mini: Binding Modalities Without Forgetting via Dense Distillation
Mohammed Irfan Kurpath, Jaseel Muhammad Kaithakkodan, Sahal Shaji Mullappilly +2
Extending a text embedding model to new modalities typically degrades text retrieval quality, and existing omni-modal embedders compensate with multi-billion parameters. We present…
Hard Vision, Easy Vision: What GPT-6 Astra Reveals Across Computer Vision
Hanoona Rasheed, Mohammed Irfan Kurpath, Bin Ren +3
Frontier general-purpose systems are rapidly expanding beyond visual understanding into capabilities traditionally handled by dedicated computer-vision models. As these capabilitie…
Small yet Assistive: Spatially-Aware Post-Training for Low Vision
Rishabh Choudhary, Shreyansh Raj, Umesh Goyal +6
An estimated 1 billion people worldwide live with vision impairment, yet current vision-language models (VLMs) produce descriptions too vague for safe navigation by blind and low-v…
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…
Ask, Solve, Generate: Self-Evolving Unified Multimodal Understanding and Generation via Self-Consistency Rewards
Ritesh Thawkar, Shravan Venkatraman, Omkar Thawakar +5
Most unified large multimodal models (LMMs) that support both visual understanding and image generation still rely on curated post-training supervision, such as human annotations,…
Paying More Attention to Visual Tokens in Self-Evolving Large Multimodal Models
Shravan Venkatraman, Ritesh Thawkar, Omkar Thawakar +4
Recently, self-evolving large multimodal models (LMMs) have received attention for improving visual reasoning in a purely unsupervised setting. However, multi-role self-play and se…
SafeDiffusion-R1: Online Reward Steering for Safe Diffusion Post-Training
Komal Kumar, Ankan Deria, Abhishek Basu +3
Diffusion models have been widely studied for removing unsafe content learned during pre-training. Existing methods require expensive supervised data, either unsafe-text paired wit…
CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning
Ankan Deria, Komal Kumar, Xilin He +4
Recent vision-language models (VLMs) typically rely on a single vision encoder trained with contrastive image-text objectives, such as CLIP-style pretraining. While contrastive enc…
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…
Mobile-O: Unified Multimodal Understanding and Generation on Mobile Device
Abdelrahman Shaker, Ahmed Heakl, Jaseel Muhammad +8
Unified multimodal models can both understand and generate visual content within a single architecture. Existing models, however, remain data-hungry and too heavy for deployment on…
Audit After Segmentation: Reference-Free Mask Quality Assessment for Language-Referred Audio-Visual Segmentation
Jinxing Zhou, Yanghao Zhou, Yaoting Wang +5
Language-referred audio-visual segmentation (Ref-AVS) aims to segment target objects described by natural language by jointly reasoning over video, audio, and text. Beyond generati…
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…
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…
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…
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…
HyRet-Change: A hybrid retentive network for remote sensing change detection
Mustansar Fiaz, Mubashir Noman, Hiyam Debary +2
Recently convolution and transformer-based change detection (CD) methods provide promising performance. However, it remains unclear how the local and global dependencies interact t…
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…
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…
ARB: A Comprehensive Arabic Multimodal Reasoning Benchmark
Sara Ghaboura, Ketan More, Wafa Alghallabi +5
As Large Multimodal Models (LMMs) become more capable, there is growing interest in evaluating their reasoning processes alongside their final outputs. However, most benchmarks rem…
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…