Visual Learning and Recognition: Difference between revisions

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David

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 Context is a 1-2% idea. Traditionally, it has only provided 1-2% of improved performance.
-When is context helpful?
+;When is context helpful?
-When there is less information.
+* Typical answer: to ''guess'' small/blurry objects based on a prior.
+* Deeper answer: to make sense of the visual world.
+** When to use context and when not to use context.
+** 80% of context is automatically handled by neural networks, but 20% of work still remains.
+;Why context is important?
+* To resolve ambiguity.
+** Even high-res objects can be ambiguous.
+** There are 30,000+ types of objects but only a few can occur in an image.
+* To notice ''unusual'' things.
+* In infer function of unknown object.
+===Pixel Context===
+Look at nearby pixels by inputting a slightly bigger region.
+===Semantic Context===
+Use other objects present to answer what is present in the target pixels.
+===Geometric Context===
+[Hoiem ''et al.'' 2005]
+Use segmentation to interpret geometry:
+* Sky
+* Ground
+* Building with normal of direction
+===Photometric Context===
+If you know where the camera is, you can estimate the size of people and cars.
+If you know where the sun is, you can estimate where the scene
+===Geographic Context===
+===Autocontext===
+Do a prediction and then feed in the output to the model again for the model to refine the prediction.
+This is similar to pose machines, hourglass networks, iterative bounding box regression.
+===Classemes===
+Descriptor is formed by concatenating outputs of weakly trained classifiers.
 ==Will be on the exam==