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

From Saliency to Camouflage Analysis in Computer Vision

University of Central Florida
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