16,990 research outputs found
Employing deep part-object relationships for salient object detection
Despite Convolutional Neural Networks (CNNs) based methods have been successful in detecting salient objects, their underlying mechanism that decides the salient intensity of each image part separately cannot avoid inconsistency of parts within the same salient object. This would ultimately result in an incomplete shape of the detected salient object. To solve this problem, we dig into part-object relationships and take the unprecedented attempt to employ these relationships endowed by the Capsule Network (CapsNet) for salient object detection. The entire salient object detection system is built directly on a Two-Stream Part-Object Assignment Network (TSPOANet) consisting of three algorithmic steps. In the first step, the learned deep feature maps of the input image are transformed to a group of primary capsules. In the second step, we feed the primary capsules into two identical streams, within each of which low-level capsules (parts) will be assigned to their familiar high-level capsules (object) via a locally connected routing. In the final step, the two streams are integrated in the form of a fully connected layer, where the relevant parts can be clustered together to form a complete salient object. Experimental results demonstrate the superiority of the proposed salient object detection network over the state-of-the-art methods
GazeDPM: Early Integration of Gaze Information in Deformable Part Models
An increasing number of works explore collaborative human-computer systems in
which human gaze is used to enhance computer vision systems. For object
detection these efforts were so far restricted to late integration approaches
that have inherent limitations, such as increased precision without increase in
recall. We propose an early integration approach in a deformable part model,
which constitutes a joint formulation over gaze and visual data. We show that
our GazeDPM method improves over the state-of-the-art DPM baseline by 4% and a
recent method for gaze-supported object detection by 3% on the public POET
dataset. Our approach additionally provides introspection of the learnt models,
can reveal salient image structures, and allows us to investigate the interplay
between gaze attracting and repelling areas, the importance of view-specific
models, as well as viewers' personal biases in gaze patterns. We finally study
important practical aspects of our approach, such as the impact of using
saliency maps instead of real fixations, the impact of the number of fixations,
as well as robustness to gaze estimation error
- …