997 research outputs found
Detecting Semantic Parts on Partially Occluded Objects
In this paper, we address the task of detecting semantic parts on partially
occluded objects. We consider a scenario where the model is trained using
non-occluded images but tested on occluded images. The motivation is that there
are infinite number of occlusion patterns in real world, which cannot be fully
covered in the training data. So the models should be inherently robust and
adaptive to occlusions instead of fitting / learning the occlusion patterns in
the training data. Our approach detects semantic parts by accumulating the
confidence of local visual cues. Specifically, the method uses a simple voting
method, based on log-likelihood ratio tests and spatial constraints, to combine
the evidence of local cues. These cues are called visual concepts, which are
derived by clustering the internal states of deep networks. We evaluate our
voting scheme on the VehicleSemanticPart dataset with dense part annotations.
We randomly place two, three or four irrelevant objects onto the target object
to generate testing images with various occlusions. Experiments show that our
algorithm outperforms several competitors in semantic part detection when
occlusions are present.Comment: Accepted to BMVC 2017 (13 pages, 3 figures
Stylized Table Tennis Robots Skill Learning with Incomplete Human Demonstrations
In recent years, Reinforcement Learning (RL) is becoming a popular technique
for training controllers for robots. However, for complex dynamic robot control
tasks, RL-based method often produces controllers with unrealistic styles. In
contrast, humans can learn well-stylized skills under supervisions. For
example, people learn table tennis skills by imitating the motions of coaches.
Such reference motions are often incomplete, e.g. without the presence of an
actual ball. Inspired by this, we propose an RL-based algorithm to train a
robot that can learn the playing style from such incomplete human
demonstrations. We collect data through the teaching-and-dragging method. We
also propose data augmentation techniques to enable our robot to adapt to balls
of different velocities. We finally evaluate our policy in different simulators
with varying dynamics.Comment: Submitted to ICRA 202
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
Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery
This paper reports a CPU-level real-time stereo matching method for surgical
images (10 Hz on 640 * 480 image with a single core of i5-9400). The proposed
method is built on the fast ''dense inverse searching'' algorithm, which
estimates the disparity of the stereo images. The overlapping image patches
(arbitrary squared image segment) from the images at different scales are
aligned based on the photometric consistency presumption. We propose a Bayesian
framework to evaluate the probability of the optimized patch disparity at
different scales. Moreover, we introduce a spatial Gaussian mixed probability
distribution to address the pixel-wise probability within the patch. In-vivo
and synthetic experiments show that our method can handle ambiguities resulted
from the textureless surfaces and the photometric inconsistency caused by the
Lambertian reflectance. Our Bayesian method correctly balances the probability
of the patch for stereo images at different scales. Experiments indicate that
the estimated depth has higher accuracy and fewer outliers than the baseline
methods in the surgical scenario
Diffusion–reaction–induced stress in moving boundary cylindrical Li-ion battery electrodes
Lithium (Li) inserted into or extracted from the electrode in Li-ion battery causes stress which may cause fracture of the electrode. A moving boundary model in a cylindrical Li-ion battery electrode accounting for reversible electrochemical reaction is obtained. The volumetric change created by Li diffusion and formation of reversible reaction product would generate the diffusion–reaction-induced stress in the electrode. The constitutive relation among Li concentration, reaction product, and stress is derived, and the numerical solutions of the concentration, reaction product, and stress fields are obtained. The effects of phase transformation and reversible electrochemical reaction on Li diffusion and stress in a cylindrical Li-ion battery electrode are analyzed
Analysis of Nakamoto Consensus, Revisited
In the Bitcoin white paper, Nakamoto proposed a very simple Byzantine fault
tolerant consensus algorithm that is also known as Nakamoto consensus. Despite
its simplicity, some existing analysis of Nakamoto consensus appears to be long
and involved. In this technical report, we aim to make such analysis simple and
transparent so that we can teach senior undergraduate students and graduate
students in our institutions. This report is largely based on a 3-hour tutorial
given by one of the authors in June 2019.Comment: 8 page
Incorporating Heterogeneous User Behaviors and Social Influences for Predictive Analysis
Behavior prediction based on historical behavioral data have practical
real-world significance. It has been applied in recommendation, predicting
academic performance, etc. With the refinement of user data description, the
development of new functions, and the fusion of multiple data sources,
heterogeneous behavioral data which contain multiple types of behaviors become
more and more common. In this paper, we aim to incorporate heterogeneous user
behaviors and social influences for behavior predictions. To this end, this
paper proposes a variant of Long-Short Term Memory (LSTM) which can consider
context information while modeling a behavior sequence, a projection mechanism
which can model multi-faceted relationships among different types of behaviors,
and a multi-faceted attention mechanism which can dynamically find out
informative periods from different facets. Many kinds of behavioral data belong
to spatio-temporal data. An unsupervised way to construct a social behavior
graph based on spatio-temporal data and to model social influences is proposed.
Moreover, a residual learning-based decoder is designed to automatically
construct multiple high-order cross features based on social behavior
representation and other types of behavior representations. Qualitative and
quantitative experiments on real-world datasets have demonstrated the
effectiveness of this model
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