742 research outputs found
Priming Neural Networks
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.Comment: fixed error in author nam
BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks
We present a simple and effective framework for simultaneous semantic
segmentation and instance segmentation with Fully Convolutional Networks
(FCNs). The method, called BiSeg, predicts instance segmentation as a posterior
in Bayesian inference, where semantic segmentation is used as a prior. We
extend the idea of position-sensitive score maps used in recent methods to a
fusion of multiple score maps at different scales and partition modes, and
adopt it as a robust likelihood for instance segmentation inference. As both
Bayesian inference and map fusion are performed per pixel, BiSeg is a fully
convolutional end-to-end solution that inherits all the advantages of FCNs. We
demonstrate state-of-the-art instance segmentation accuracy on PASCAL VOC.Comment: BMVC201
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
How do we learn an object detector that is invariant to occlusions and
deformations? Our current solution is to use a data-driven strategy -- collect
large-scale datasets which have object instances under different conditions.
The hope is that the final classifier can use these examples to learn
invariances. But is it really possible to see all the occlusions in a dataset?
We argue that like categories, occlusions and object deformations also follow a
long-tail. Some occlusions and deformations are so rare that they hardly
happen; yet we want to learn a model invariant to such occurrences. In this
paper, we propose an alternative solution. We propose to learn an adversarial
network that generates examples with occlusions and deformations. The goal of
the adversary is to generate examples that are difficult for the object
detector to classify. In our framework both the original detector and adversary
are learned in a joint manner. Our experimental results indicate a 2.3% mAP
boost on VOC07 and a 2.6% mAP boost on VOC2012 object detection challenge
compared to the Fast-RCNN pipeline. We also release the code for this paper.Comment: CVPR 2017 Camera Read
Video Object Detection with an Aligned Spatial-Temporal Memory
We introduce Spatial-Temporal Memory Networks for video object detection. At
its core, a novel Spatial-Temporal Memory module (STMM) serves as the recurrent
computation unit to model long-term temporal appearance and motion dynamics.
The STMM's design enables full integration of pretrained backbone CNN weights,
which we find to be critical for accurate detection. Furthermore, in order to
tackle object motion in videos, we propose a novel MatchTrans module to align
the spatial-temporal memory from frame to frame. Our method produces
state-of-the-art results on the benchmark ImageNet VID dataset, and our
ablative studies clearly demonstrate the contribution of our different design
choices. We release our code and models at
http://fanyix.cs.ucdavis.edu/project/stmn/project.html
Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems
Findings in recent years on the sensitivity of convolutional neural networks
to additive noise, light conditions and to the wholeness of the training
dataset, indicate that this technology still lacks the robustness needed for
the autonomous robotic industry. In an attempt to bring computer vision
algorithms closer to the capabilities of a human operator, the mechanisms of
the human visual system was analyzed in this work. Recent studies show that the
mechanisms behind the recognition process in the human brain include continuous
generation of predictions based on prior knowledge of the world. These
predictions enable rapid generation of contextual hypotheses that bias the
outcome of the recognition process. This mechanism is especially advantageous
in situations of uncertainty, when visual input is ambiguous. In addition, the
human visual system continuously updates its knowledge about the world based on
the gaps between its prediction and the visual feedback. Convolutional neural
networks are feed forward in nature and lack such top-down contextual
attenuation mechanisms. As a result, although they process massive amounts of
visual information during their operation, the information is not transformed
into knowledge that can be used to generate contextual predictions and improve
their performance. In this work, an architecture was designed that aims to
integrate the concepts behind the top-down prediction and learning processes of
the human visual system with the state of the art bottom-up object recognition
models, e.g., deep convolutional neural networks. The work focuses on two
mechanisms of the human visual system: anticipation-driven perception and
reinforcement-driven learning. Imitating these top-down mechanisms, together
with the state of the art bottom-up feed-forward algorithms, resulted in an
accurate, robust, and continuously improving target recognition model
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