From Saliency to Camouflage Analysis in Computer Vision

Explore the evolution of visual saliency and camouflage analysis in computer vision through this comprehensive 47-minute lecture. Delve into the advancements of Artificial Intelligence and its impact on Computer Vision, focusing on visual saliency detection techniques. Learn about FCN-based salient object detection methods, the proposed framework for saliency analysis, and its practical applications. Discover the challenges and current research directions in camouflaged object segmentation, understanding the differences between salient and camouflaged objects. Gain insights into evaluation metrics, experimental results, and potential future developments in this field. Presented by Dr. Tam Nguyen from the University of Dayton, this talk offers a deep dive into cutting-edge research and potential funding opportunities for those interested in pursuing further studies in visual perception and object detection.

Intro
Outline
Overview
The advancement of Artificial Intelligence
How about Computer Vision?
What can we do?
Visual Saliency: what is it?
FCN is coming out
FCN-based salient object detection methods
What can we do to detect salient object?
Stimulus revisit
Motivation
Proposed framework
Semantic Extraction
Explicit Saliency Map
Implicit Saliency Map
Saliency Fusion
Why do we need two maps?
Evaluation on HKUIS Dataset
Applications of Saliency Analysis
What's next?
We are talking about camouflaged objects
Why do we need camouflaged object segmentation?
Salient Objects vs. Camouflaged Objects
Why is salient object segmentation feasible?
What are the problems in camouflage analysis?
Related Work
Segmentation Branch
Classification Branch
Evaluation Metrics
Experimental Results
Current Research Directions
Future Plan
Potential Funding Agencies
Collaborators
Thank you very much for your attention!