4 citations · 8 across the 11 of their papers we have counts for
12 papers
Falcon Perception-HD: High Density Perception via Reinforcement Learning
Sofian Chaybouti, Yasser Dahou, Ngoc Dung Huynh +2
Autoregressive perception models trained to localize visual entities under the open-vocabulary setting are mostly trained using Supervised fine-tuning (SFT) with maximum likelihood…
Falcon Perception
Aviraj Bevli, Sofian Chaybouti, Yasser Dahou +6
Perception-centric systems are typically implemented with a modular encoder-decoder pipeline: a vision backbone for feature extraction and a separate decoder (or late-fusion module…
VisRes Bench: On Evaluating the Visual Reasoning Capabilities of VLMs
Brigitta Malagurski Törtei, Yasser Dahou, Ngoc Dung Huynh +5
Vision-Language Models (VLMs) have achieved remarkable progress across tasks such as visual question answering and image captioning. Yet, the extent to which these models perform v…
SigLino: Efficient Multi-Teacher Distillation for Agglomerative Vision Foundation Models
Sofian Chaybouti, Sanath Narayan, Yasser Dahou +6
Vision foundation models trained via multi-teacher distillation offer a promising path toward unified visual representations, yet the learning dynamics and data efficiency of such…
Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Jingwei Zuo, Maksim Velikanov, Ilyas Chahed +24
In this report, we introduce Falcon-H1, a new series of large language models (LLMs) featuring hybrid architecture designs optimized for both high performance and efficiency across…
Vision-Language Models Can't See the Obvious
Yasser Dahou, Ngoc Dung Huynh, Phuc H. Le-Khac +3
We present Saliency Benchmark (SalBench), a novel benchmark designed to assess the capability of Large Vision-Language Models (LVLM) in detecting visually salient features that are…
SVLA: A Unified Speech-Vision-Language Assistant with Multimodal Reasoning and Speech Generation
Ngoc Dung Huynh, Mohamed Reda Bouadjenek, Imran Razzak +2
Large vision and language models show strong performance in tasks like image captioning, visual question answering, and retrieval. However, challenges remain in integrating speech,…
Visual question answering: from early developments to recent advances -- a survey
Ngoc Dung Huynh, Mohamed Reda Bouadjenek, Sunil Aryal +2
Visual Question Answering (VQA) is an evolving research field aimed at enabling machines to answer questions about visual content by integrating image and language processing techn…
SimpsonsVQA: Enhancing Inquiry-Based Learning with a Tailored Dataset
Ngoc Dung Huynh, Mohamed Reda Bouadjenek, Sunil Aryal +2
Visual Question Answering (VQA) has emerged as a promising area of research to develop AI-based systems for enabling interactive and immersive learning. Numerous VQA datasets have…
HUMS2023 Data Challenge Result Submission
Dhiraj Neupane, Lakpa Dorje Tamang, Ngoc Dung Huynh +2
We implemented a simple method for early detection in this research. The implemented methods are plotting the given mat files and analyzing scalogram images generated by performing…
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
Luuk H. Boulogne, Julian Lorenz, Daniel Kienzle +35
Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Publ…
Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey
Ngoc Dung Huynh, Mohamed Reda Bouadjenek, Imran Razzak +4
A Machine-Critical Application is a system that is fundamentally necessary to the success of specific and sensitive operations such as search and recovery, rescue, military, and em…