Visual priming is known to affect the human visual system to allow detection of scene elements, even those that may have been near unnoticeable before, such as the presence of camouflaged animals. This process has been shown to be an effect of top-down signaling in the visual system triggered by the said cue. In this paper, we propose a mechanism to mimic the process of priming in the context of object detection and segmentation. We view priming as having a modulatory, cue dependent effect on layers of features within a network. Our results show how such a process can be complementary to, and at times more effective than simple post-processing applied to the output of the network, notably so in cases where the object is hard to detect such as in severe noise. Moreover, we find the effects of priming are sometimes stronger when early visual layers are affected. Overall, our experiments confirm that top-down signals can go a long way in improving object detection and segmentation.
Priming is a nonconscious form of human memory, which is concerned with perceptual identification of words and objects and which has only recently been recognized as separate from other forms of memory or memory systems. It is currently under intense experimental scrutiny. Evidence is converging for the proposition that priming is an expression of a perceptual representation system that operates at a pre-semantic level; it emerges early in development, and access to it lacks the kind of flexibility characteristic of other cognitive memory systems. Conceptual priming, however, seems to be based on the operations of semantic memory.  The conclusion that scene knowledge interacts with object perception depends on evidence that object detection is facilitated by consistent scene context. Experiment 1 replicated the I. Biederman, R. J. Mezzanotte, and J. C. Rabinowitz (1982) object-detection paradigm. Detection performance was higher for semantically consistent versus inconsistent objects. However, when the paradigm was modified to control for response bias (Experiments 2 and 3) or when response bias was eliminated by means of a forced-choice procedure (Experiment 4), no such advantage obtained. When an additional source of biasing information was eliminated by presenting the object label after the scene (Experiments 3 and 4), there was either no effect of consistency (Experiment 4) or an inconsistent object advantage (Experiment 3). These results suggest that object perception is not facilitated by consistent scene context. Repetition priming is a nonconscious form of memory that is accompanied by reductions in neural activity when an experience is repeated. To date, however, there is no direct evidence that these neural reductions underlie the behavioral advantage afforded to repeated material. Here we demonstrate a causal linkage between neural and behavioral priming in humans. fMRI (functional magnetic resonance imaging) was used in combination with transcranial magnetic stimulation (TMS) to target and disrupt activity in the left frontal cortex during repeated classification of objects. Left-frontal TMS disrupted both the neural and behavioral markers of priming. Neural priming in early sensory regions was unaffected by left-frontal TMS—a finding that provides evidence for separable conceptual and perceptual components of priming. State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available. The field of object detection has seen dramatic performance improvements in the last few years. Most of these gains are attributed to bottom-up, feedforward ConvNet frameworks. However, in case of humans, top-down information, context and feedback play an important role in doing object detection. This paper investigates how we can incorporate top-down information and feedback in the state-of-the-art Faster R-CNN framework. Specifically, we propose to: (a) augment Faster R-CNN with a semantic segmentation network; (b) use segmentation for top-down contextual priming; (c) use segmentation to provide top-down iterative feedback using two stage training. Our results indicate that all three contributions improve the performance on object detection, semantic segmentation and region proposal generation.