Dense CRFs (Krähenbühl & Koller, 2011) connect all pairs of pixels with a Gaussian edge-weight function, enabling accurate boundary recovery. Mean-field inference approximates the CRF efficiently using high-dimensional filtering. CRFs are standard in:
Beyond pixels, a scene graph encodes objects (nodes) and relationships (edges), e.g., "person - sitting on - chair." Graph networks reason about these relationships for visual question answering and image captioning. Dense CRFs (Krähenbühl & Koller, 2011) connect all
In essence, this field turned digital imaging from a study of "what color is this dot?" into a study of Dense CRFs (Krähenbühl & Koller
Dense CRFs (Krähenbühl & Koller, 2011) connect all pairs of pixels with a Gaussian edge-weight function, enabling accurate boundary recovery. Mean-field inference approximates the CRF efficiently using high-dimensional filtering. CRFs are standard in:
Beyond pixels, a scene graph encodes objects (nodes) and relationships (edges), e.g., "person - sitting on - chair." Graph networks reason about these relationships for visual question answering and image captioning.
In essence, this field turned digital imaging from a study of "what color is this dot?" into a study of