Top-down attention guided object detection

Existing attention models concentrate on bottom-up attention guidance, and lack of effective definition of top-down attention information. In this paper we define a new holistic scene representation and use it as top-down attention information which works in three ways. The first is to discriminate between close-up and open scene categories. The second and the third are to provide reliable priors for the presence or absence of object and the location of it. Compared with traditional attention guidance algorithms, our algorithm shows how scene classification and basing directly on entire scene without segmentation stages, facilitate the object detection. Two stages of pre-attention and focus attention enhance the detecting performance and are more suitable for vision information processing in high level. Experiment results prove the effectiveness of our algorithm.