6,391 research outputs found
Robustness of Object Recognition under Extreme Occlusion in Humans and Computational Models
Most objects in the visual world are partially occluded, but humans can
recognize them without difficulty. However, it remains unknown whether object
recognition models like convolutional neural networks (CNNs) can handle
real-world occlusion. It is also a question whether efforts to make these
models robust to constant mask occlusion are effective for real-world
occlusion. We test both humans and the above-mentioned computational models in
a challenging task of object recognition under extreme occlusion, where target
objects are heavily occluded by irrelevant real objects in real backgrounds.
Our results show that human vision is very robust to extreme occlusion while
CNNs are not, even with modifications to handle constant mask occlusion. This
implies that the ability to handle constant mask occlusion does not entail
robustness to real-world occlusion. As a comparison, we propose another
computational model that utilizes object parts/subparts in a compositional
manner to build robustness to occlusion. This performs significantly better
than CNN-based models on our task with error patterns similar to humans. These
findings suggest that testing under extreme occlusion can better reveal the
robustness of visual recognition, and that the principle of composition can
encourage such robustness.Comment: To be presented at the 41st Annual Meeting of the Cognitive Science
Societ
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
Visual Concepts and Compositional Voting
It is very attractive to formulate vision in terms of pattern theory
\cite{Mumford2010pattern}, where patterns are defined hierarchically by
compositions of elementary building blocks. But applying pattern theory to real
world images is currently less successful than discriminative methods such as
deep networks. Deep networks, however, are black-boxes which are hard to
interpret and can easily be fooled by adding occluding objects. It is natural
to wonder whether by better understanding deep networks we can extract building
blocks which can be used to develop pattern theoretic models. This motivates us
to study the internal representations of a deep network using vehicle images
from the PASCAL3D+ dataset. We use clustering algorithms to study the
population activities of the features and extract a set of visual concepts
which we show are visually tight and correspond to semantic parts of vehicles.
To analyze this we annotate these vehicles by their semantic parts to create a
new dataset, VehicleSemanticParts, and evaluate visual concepts as unsupervised
part detectors. We show that visual concepts perform fairly well but are
outperformed by supervised discriminative methods such as Support Vector
Machines (SVM). We next give a more detailed analysis of visual concepts and
how they relate to semantic parts. Following this, we use the visual concepts
as building blocks for a simple pattern theoretical model, which we call
compositional voting. In this model several visual concepts combine to detect
semantic parts. We show that this approach is significantly better than
discriminative methods like SVM and deep networks trained specifically for
semantic part detection. Finally, we return to studying occlusion by creating
an annotated dataset with occlusion, called VehicleOcclusion, and show that
compositional voting outperforms even deep networks when the amount of
occlusion becomes large.Comment: It is accepted by Annals of Mathematical Sciences and Application
iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects
We address the task of 6D pose estimation of known rigid objects from single
input images in scenarios where the objects are partly occluded. Recent
RGB-D-based methods are robust to moderate degrees of occlusion. For RGB
inputs, no previous method works well for partly occluded objects. Our main
contribution is to present the first deep learning-based system that estimates
accurate poses for partly occluded objects from RGB-D and RGB input. We achieve
this with a new instance-aware pipeline that decomposes 6D object pose
estimation into a sequence of simpler steps, where each step removes specific
aspects of the problem. The first step localizes all known objects in the image
using an instance segmentation network, and hence eliminates surrounding
clutter and occluders. The second step densely maps pixels to 3D object surface
positions, so called object coordinates, using an encoder-decoder network, and
hence eliminates object appearance. The third, and final, step predicts the 6D
pose using geometric optimization. We demonstrate that we significantly
outperform the state-of-the-art for pose estimation of partly occluded objects
for both RGB and RGB-D input
Classification of Occluded Objects using Fast Recurrent Processing
Recurrent neural networks are powerful tools for handling incomplete data
problems in computer vision, thanks to their significant generative
capabilities. However, the computational demand for these algorithms is too
high to work in real time, without specialized hardware or software solutions.
In this paper, we propose a framework for augmenting recurrent processing
capabilities into a feedforward network without sacrificing much from
computational efficiency. We assume a mixture model and generate samples of the
last hidden layer according to the class decisions of the output layer, modify
the hidden layer activity using the samples, and propagate to lower layers. For
visual occlusion problem, the iterative procedure emulates feedforward-feedback
loop, filling-in the missing hidden layer activity with meaningful
representations. The proposed algorithm is tested on a widely used dataset, and
shown to achieve 2 improvement in classification accuracy for occluded
objects. When compared to Restricted Boltzmann Machines, our algorithm shows
superior performance for occluded object classification.Comment: arXiv admin note: text overlap with arXiv:1409.8576 by other author
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