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Inspired my work in Self-supervised Representation Learning
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Classroom Contents
Why It Pays to Study Psychology - Lessons from Computer Vision
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- 1 Intro
- 2 When are two textures similar?
- 3 Texture Analysis
- 4 Béla Julesz, father of texture
- 5 Texton Discrimination (Julesz)
- 6 Texture Synthesis input image
- 7 Scene Classification (Renninger & Malik)
- 8 Texton Histogram Matching
- 9 Discrimination of Basic Categories
- 10 Scene Recognition using Texture (2001)
- 11 Object Recognition is just Texture Recognition
- 12 Need learning to handle complexity!
- 13 Qualitative 3D Scene Reasoning
- 14 Support
- 15 Position, Probability, Size
- 16 3D Spatial Layout
- 17 Our Main Challenge
- 18 Infer Most Likely Scene
- 19 Qualitative 3D surfaces
- 20 The World Behind the image
- 21 Qualitative vs. Quantitative 3D
- 22 Occlusion Reasoning is Necessary
- 23 Line Labeling [Clowes 1971, Huffman 1971; Waltz 1972; Malik 1986]
- 24 Are Junctions local evidence?
- 25 Recover Major Occlusions
- 26 3D Depth Cues for Occlusion
- 27 Geometrically Coherent Image Interpretation
- 28 The Problem with Labeling
- 29 Simple 3D renderings
- 30 Capacity of Visual Long Term Memory (Aude Oliva)
- 31 how far can we push the fidelity of visual LTM representation ? Same object, different states
- 32 Inspired my work in Self-supervised Representation Learning
