Learning to Combine Bottom-Up and Top-Down Segmentation

Bottom-up, top-down

Learning to Attend - From Bottom-Up to Top-Down

The control of overt visual attention relies on an interplay of bottom-up and top-down mechanisms. Purely bottom-up models may provide a reasonable account of the looking behaviors of young infants, but they cannot accurately account for attention orienting of adults in many natural behaviors. But how do humans learn to incorporate topdown mechanisms into their control of attention? The phenome...

Unsupervised Tattoo Segmentation Combining Bottom-Up and Top-Down Cues

Tattoo segmentation is challenging due to the complexity and large variance in tattoo structures. We have developed a segmentation algorithm for finding tattoos in an image. Our basic idea is split-merge: split each tattoo image into clusters through a bottom-up process, learn to merge the clusters containing skin and then distinguish tattoo from the other skin via top-down prior in the image i...

Bottom-Up Beats Top-Down

In PEPA, the calculation of the transitions enabled by a process accounts for a large part of the time for the state space exploration of the underlying Markov chain. Unlike other approaches based on recursion, we present a new technique that is iterative—it traverses the process’ binary tree from the sequential components at the leaves up to the root. Empirical results shows that this algorith...

Top-down and bottom-up influences on learning from animations

To evaluate how top-down and bottom-up processes contribute to learning from animated displays, we conducted four experiments that varied either in the design of animations or the prior knowledge of the learners. Experiments 1–3 examined whether adding interactivity and signaling to an animation benefits learners in developing a mental model of a mechanical system. Although learners utilized in...