152 citations · 306 across the 37 of their papers we have counts for
12 papers · 1 filter
PIAD-SRNN: Physics-Informed Adaptive Decomposition in State-Space RNN
Ahmad Mohammadshirazi, Pinaki Prasad Guha Neogi, Rajiv Ramnath
Time series forecasting often demands a trade-off between accuracy and efficiency. While recent Transformer models have improved forecasting capabilities, they come with high compu…
DLaVA: Document Language and Vision Assistant for Answer Localization with Enhanced Interpretability and Trustworthiness
Ahmad Mohammadshirazi, Pinaki Prasad Guha Neogi, Ser-Nam Lim +1
Document Visual Question Answering (VQA) demands robust integration of text detection, recognition, and spatial reasoning to interpret complex document layouts. In this work, we in…
Scalable Deep Metric Learning on Attributed Graphs
Xiang Li, Gagan Agrawal, Ruoming Jin +1
We consider the problem of constructing embeddings of large attributed graphs and supporting multiple downstream learning tasks. We develop a graph embedding method, which is based…
Federated Contrastive Learning of Graph-Level Representations
Xiang Li, Gagan Agrawal, Rajiv Ramnath +1
Graph-level representations (and clustering/classification based on these representations) are required in a variety of applications. Examples include identifying malicious network…
KnobGen: Controlling the Sophistication of Artwork in Sketch-Based Diffusion Models
Pouyan Navard, Amin Karimi Monsefi, Mengxi Zhou +3
Recent advances in diffusion models have significantly improved text-to-image (T2I) generation, but they often struggle to balance fine-grained precision with high-level control. M…
Frequency-Guided Masking for Enhanced Vision Self-Supervised Learning
Amin Karimi Monsefi, Mengxi Zhou, Nastaran Karimi Monsefi +3
We present a novel frequency-based Self-Supervised Learning (SSL) approach that significantly enhances its efficacy for pre-training. Prior work in this direction masks out pre-def…
DetailCLIP: Detail-Oriented CLIP for Fine-Grained Tasks
Amin Karimi Monsefi, Kishore Prakash Sailaja, Ali Alilooee +2
In this paper, we introduce DetailCLIP: A Detail-Oriented CLIP to address the limitations of contrastive learning-based vision-language models, particularly CLIP, in handling detai…
DocParseNet: Advanced Semantic Segmentation and OCR Embeddings for Efficient Scanned Document Annotation
Ahmad Mohammadshirazi, Ali Nosrati Firoozsalari, Mengxi Zhou +2
Automating the annotation of scanned documents is challenging, requiring a balance between computational efficiency and accuracy. DocParseNet addresses this by combining deep learn…
Recent Advances in Traffic Accident Analysis and Prediction: A Comprehensive Review of Machine Learning Techniques
Noushin Behboudi, Sobhan Moosavi, Rajiv Ramnath
Traffic accidents pose a severe global public health issue, leading to 1.19 million fatalities annually, with the greatest impact on individuals aged 5 to 29 years old. This paper…
Efficient and Interpretable Information Retrieval for Product Question Answering with Heterogeneous Data
Biplob Biswas, Rajiv Ramnath
Expansion-enhanced sparse lexical representation improves information retrieval (IR) by minimizing vocabulary mismatch problems during lexical matching. In this paper, we explore t…
CrashFormer: A Multimodal Architecture to Predict the Risk of Crash
Amin Karimi Monsefi, Pouya Shiri, Ahmad Mohammadshirazi +4
Reducing traffic accidents is a crucial global public safety concern. Accident prediction is key to improving traffic safety, enabling proactive measures to be taken before a crash…
Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain
Amin Karimi Monsefi, Payam Karisani, Mengxi Zhou +5
Standard modern machine-learning-based imaging methods have faced challenges in medical applications due to the high cost of dataset construction and, thereby, the limited labeled…