260 citations · 268 across the 9 of their papers we have counts for
10 papers
Agentic AI Enhances Physician Trust in Clinical Decision Making
Zhiling Yan, Zhe Fang, David J King +10
Medical AI has shifted from reasoning to agentic AI, a new paradigm that autonomously invokes external tools during reasoning, rendering intermediate reasoning steps and tool outpu…
RemEdit: Efficient Diffusion Editing with Riemannian Geometry
Eashan Adhikarla, Brian D. Davison
Controllable image generation is fundamental to the success of modern generative AI, yet it faces a critical trade-off between semantic fidelity and inference speed. The RemEdit di…
Diffusion Models for Low-Light Image Enhancement: A Multi-Perspective Taxonomy and Performance Analysis
Eashan Adhikarla, Yixin Liu, Brian D. Davison
Low-light image enhancement (LLIE) is vital for safety-critical applications such as surveillance, autonomous navigation, and medical imaging, where visibility degradation can impa…
LENVIZ: A High-Resolution Low-Exposure Night Vision Benchmark Dataset
Manjushree Aithal, Rosaura G. VidalMata, Manikandtan Kartha +6
Low-light image enhancement is crucial for a myriad of applications, from night vision and surveillance, to autonomous driving. However, due to the inherent limitations that come i…
From Darkness to Detail: Frequency-Aware SSMs for Low-Light Vision
Eashan Adhikarla, Kai Zhang, Gong Chen +2
Low-light image enhancement remains a persistent challenge in computer vision, where state-of-the-art models are often hampered by hardware constraints and computational inefficien…
Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure Image Enhancement
Eashan Adhikarla, Kai Zhang, Rosaura G. VidalMata +5
Despite recent strides made by AI in image processing, the issue of mixed exposure, pivotal in many real-world scenarios like surveillance and photography, remains inadequately add…
Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras
Jun Yu, Yutong Dai, Xiaokang Liu +14
MTL is a learning paradigm that effectively leverages both task-specific and shared information to address multiple related tasks simultaneously. In contrast to STL, MTL offers a s…
Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shifts
Eashan Adhikarla, Kai Zhang, Jun Yu +3
AI applications are becoming increasingly visible to the general public. There is a notable gap between the theoretical assumptions researchers make about computer vision models an…
BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks
Kai Zhang, Rong Zhou, Eashan Adhikarla +20
Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to ut…
Memory Defense: More Robust Classification via a Memory-Masking Autoencoder
Eashan Adhikarla, Dan Luo, Brian D. Davison
Many deep neural networks are susceptible to minute perturbations of images that have been carefully crafted to cause misclassification. Ideally, a robust classifier would be immun…