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Light-weight camera localization in existing maps is essential for
vision-based navigation. Currently, visual and visual-inertial odometry
(VO\&VIO) techniques are well-developed for state estimation but with
inevitable accumulated drifts and pose jumps upon loop closure. To overcome
these problems, we propose an efficient monocular camera localization method in
prior LiDAR maps using direct 2D-3D line correspondences. To handle the
appearance differences and modality gaps between LiDAR point clouds and images,
geometric 3D lines are extracted offline from LiDAR maps while robust 2D lines
are extracted online from video sequences. With the pose prediction from VIO,
we can efficiently obtain coarse 2D-3D line correspondences. Then the camera
poses and 2D-3D correspondences are iteratively optimized by minimizing the
projection error of correspondences and rejecting outliers. Experimental
results on the EurocMav dataset and our collected dataset demonstrate that the
proposed method can efficiently estimate camera poses without accumulated
drifts or pose jumps in structured environments. | [
"cs.CV"
] |
In this study, we investigate the use of global information to speed up the
learning process and increase the cumulative rewards of reinforcement learning
(RL) in competition tasks. Within the actor-critic RL, we introduce multiple
cooperative critics from two levels of the hierarchy and propose a
reinforcement learning from hierarchical critics (RLHC) algorithm. In our
approach, each agent receives value information from local and global critics
regarding a competition task and accesses multiple cooperative critics in a
top-down hierarchy. Thus, each agent not only receives low-level details but
also considers coordination from higher levels, thereby obtaining global
information to improve the training performance. Then, we test the proposed
RLHC algorithm against the benchmark algorithm, proximal policy optimisation
(PPO), for two experimental scenarios performed in a Unity environment
consisting of tennis and soccer agents' competitions. The results showed that
RLHC outperforms the benchmark on both competition tasks. | [
"cs.LG",
"cs.MA",
"stat.ML"
] |
We describe nonparametric deconvolution models (NDMs), a family of Bayesian
nonparametric models for collections of data in which each observation is the
average over the features from heterogeneous particles. For example, these
types of data are found in elections, where we observe precinct-level vote
tallies (observations) of individual citizens' votes (particles) across each of
the candidates or ballot measures (features), where each voter is part of a
specific voter cohort or demographic (factor). Like the hierarchical Dirichlet
process, NDMs rely on two tiers of Dirichlet processes to explain the data with
an unknown number of latent factors; each observation is modeled as a weighted
average of these latent factors. Unlike existing models, NDMs recover how
factor distributions vary locally for each observation. This uniquely allows
NDMs both to deconvolve each observation into its constituent factors, and also
to describe how the factor distributions specific to each observation vary
across observations and deviate from the corresponding global factors. We
present variational inference techniques for this family of models and study
its performance on simulated data and voting data from California. We show that
including local factors improves estimates of global factors and provides a
novel scaffold for exploring data. | [
"cs.LG",
"stat.ML",
"I.5.1"
] |
We present a novel deep reinforcement learning method to learn construction
heuristics for vehicle routing problems. In specific, we propose a
Multi-Decoder Attention Model (MDAM) to train multiple diverse policies, which
effectively increases the chance of finding good solutions compared with
existing methods that train only one policy. A customized beam search strategy
is designed to fully exploit the diversity of MDAM. In addition, we propose an
Embedding Glimpse layer in MDAM based on the recursive nature of construction,
which can improve the quality of each policy by providing more informative
embeddings. Extensive experiments on six different routing problems show that
our method significantly outperforms the state-of-the-art deep learning based
models. | [
"cs.LG",
"cs.AI"
] |
Setting sale prices correctly is of great importance for firms, and the study
and forecast of prices time series is therefore a relevant topic not only from
a data science perspective but also from an economic and applicative one. In
this paper we examine different techniques to forecast sale prices applied by
an Italian food wholesaler, as a step towards the automation of pricing tasks
usually taken care by human workforce. We consider ARIMA models and compare
them to Prophet, a scalable forecasting tool by Facebook based on a generalized
additive model, and to deep learning models exploiting Long Short--Term Memory
(LSTM) and Convolutional Neural Networks (CNNs). ARIMA models are frequently
used in econometric analyses, providing a good benchmark for the problem under
study. Our results indicate that ARIMA models and LSTM neural networks perform
similarly for the forecasting task under consideration, while the combination
of CNNs and LSTMs attains the best overall accuracy, but requires more time to
be tuned. On the contrary, Prophet is quick and easy to use, but considerably
less accurate.t overall accuracy, but requires more time to be tuned. On the
contrary, Prophet is quick and easy to use, but considerably less accurate. | [
"cs.LG",
"stat.AP"
] |
Several supermodular losses have been shown to improve the perceptual quality
of image segmentation in a discriminative framework such as a structured output
support vector machine (SVM). These loss functions do not necessarily have the
same structure as the one used by the segmentation inference algorithm, and in
general, we may have to resort to generic submodular minimization algorithms
for loss augmented inference. Although these come with polynomial time
guarantees, they are not practical to apply to image scale data. Many
supermodular losses come with strong optimization guarantees, but are not
readily incorporated in a loss augmented graph cuts procedure. This motivates
our strategy of employing the alternating direction method of multipliers
(ADMM) decomposition for loss augmented inference. In doing so, we create a new
API for the structured SVM that separates the maximum a posteriori (MAP)
inference of the model from the loss augmentation during training. In this way,
we gain computational efficiency, making new choices of loss functions
practical for the first time, while simultaneously making the inference
algorithm employed during training closer to the test time procedure. We show
improvement both in accuracy and computational performance on the Microsoft
Research Grabcut database and a brain structure segmentation task, empirically
validating the use of several supermodular loss functions during training, and
the improved computational properties of the proposed ADMM approach over the
Fujishige-Wolfe minimum norm point algorithm. | [
"cs.CV"
] |
A new approach for tuning the parameters of MultiScale Retinex (MSR) based
color image enhancement algorithm using a popular optimization method, namely,
Particle Swarm Optimization (PSO) is presented in this paper. The image
enhancement using MSR scheme heavily depends on parameters such as Gaussian
surround space constant, number of scales, gain and offset etc. Selection of
these parameters, empirically and its application to MSR scheme to produce
inevitable results are the major blemishes. The method presented here results
in huge savings of computation time as well as improvement in the visual
quality of an image, since the PSO exploited maximizes the MSR parameters. The
objective of PSO is to validate the visual quality of the enhanced image
iteratively using an effective objective criterion based on entropy and edge
information of an image. The PSO method of parameter optimization of MSR scheme
achieves a very good quality of reconstructed images, far better than that
possible with the other existing methods. Finally, the quality of the enhanced
color images obtained by the proposed method are evaluated using novel metric,
namely, Wavelet Energy (WE). The experimental results presented show that color
images enhanced using the proposed scheme are clearer, more vivid and
efficient. | [
"cs.CV",
"68T45",
"H.2.0"
] |
Scene flow is a challenging task aimed at jointly estimating the 3D structure
and motion of the sensed environment. Although deep learning solutions achieve
outstanding performance in terms of accuracy, these approaches divide the whole
problem into standalone tasks (stereo and optical flow) addressing them with
independent networks. Such a strategy dramatically increases the complexity of
the training procedure and requires power-hungry GPUs to infer scene flow
barely at 1 FPS. Conversely, we propose DWARF, a novel and lightweight
architecture able to infer full scene flow jointly reasoning about depth and
optical flow easily and elegantly trainable end-to-end from scratch. Moreover,
since ground truth images for full scene flow are scarce, we propose to
leverage on the knowledge learned by networks specialized in stereo or flow,
for which much more data are available, to distill proxy annotations.
Exhaustive experiments show that i) DWARF runs at about 10 FPS on a single
high-end GPU and about 1 FPS on NVIDIA Jetson TX2 embedded at KITTI resolution,
with moderate drop in accuracy compared to 10x deeper models, ii) learning from
many distilled samples is more effective than from the few, annotated ones
available. Code available at:
https://github.com/FilippoAleotti/Dwarf-Tensorflow | [
"cs.CV",
"cs.RO"
] |
Self-supervised learning aims to learn representations from the data itself
without explicit manual supervision. Existing efforts ignore a crucial aspect
of self-supervised learning - the ability to scale to large amount of data
because self-supervision requires no manual labels. In this work, we revisit
this principle and scale two popular self-supervised approaches to 100 million
images. We show that by scaling on various axes (including data size and
problem 'hardness'), one can largely match or even exceed the performance of
supervised pre-training on a variety of tasks such as object detection, surface
normal estimation (3D) and visual navigation using reinforcement learning.
Scaling these methods also provides many interesting insights into the
limitations of current self-supervised techniques and evaluations. We conclude
that current self-supervised methods are not 'hard' enough to take full
advantage of large scale data and do not seem to learn effective high level
semantic representations. We also introduce an extensive benchmark across 9
different datasets and tasks. We believe that such a benchmark along with
comparable evaluation settings is necessary to make meaningful progress. Code
is at: https://github.com/facebookresearch/fair_self_supervision_benchmark. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
As a common visual problem, co-saliency detection within a single image does
not attract enough attention and yet has not been well addressed. Existing
methods often follow a bottom-up strategy to infer co-saliency in an image,
where salient regions are firstly detected using visual primitives such as
color and shape, and then grouped and merged into a co-saliency map. However,
co-saliency is intrinsically perceived in a complex manner with bottom-up and
top-down strategies combined in human vision. To deal with this problem, a
novel end-to-end trainable network is proposed in this paper, which includes a
backbone net and two branch nets. The backbone net uses ground-truth masks as
top-down guidance for saliency prediction, while the two branch nets construct
triplet proposals for feature organization and clustering, which drives the
network to be sensitive to co-salient regions in a bottom-up way. To evaluate
the proposed method, we construct a new dataset of 2,019 nature images with
co-saliency in each image. Experimental results show that the proposed method
achieves a state-of-the-art accuracy with a running speed of 28fps. | [
"cs.CV"
] |
In this paper, we propose a novel unsupervised color constancy method, called
Probabilistic Color Constancy (PCC). We define a framework for estimating the
illumination of a scene by weighting the contribution of different image
regions using a graph-based representation of the image. To estimate the weight
of each (super-)pixel, we rely on two assumptions: (Super-)pixels with similar
colors contribute similarly and darker (super-)pixels contribute less. The
resulting system has one global optimum solution. The proposed method achieves
competitive performance, compared to the state-of-the-art, on INTEL-TAU
dataset. | [
"cs.CV",
"eess.IV"
] |
In this work, we present a novel approach for training Generative Adversarial
Networks (GANs). Using the attention maps produced by a Teacher- Network we are
able to improve the quality of the generated images as well as perform weakly
object localization on the generated images. To this end, we generate images of
HEp-2 cells captured with Indirect Imunofluoresence (IIF) and study the ability
of our network to perform a weakly localization of the cell. Firstly, we
demonstrate that whilst GANs can learn the mapping between the input domain and
the target distribution efficiently, the discriminator network is not able to
detect the regions of interest. Secondly, we present a novel attention transfer
mechanism which allows us to enforce the discriminator to put emphasis on the
regions of interest via transfer learning. Thirdly, we show that this leads to
more realistic images, as the discriminator learns to put emphasis on the area
of interest. Fourthly, the proposed method allows one to generate both images
as well as attention maps which can be useful for data annotation e.g in object
detection. | [
"cs.CV"
] |
Recently, AutoRegressive (AR) models for the whole image generation empowered
by transformers have achieved comparable or even better performance to
Generative Adversarial Networks (GANs). Unfortunately, directly applying such
AR models to edit/change local image regions, may suffer from the problems of
missing global information, slow inference speed, and information leakage of
local guidance. To address these limitations, we propose a novel model -- image
Local Autoregressive Transformer (iLAT), to better facilitate the locally
guided image synthesis. Our iLAT learns the novel local discrete
representations, by the newly proposed local autoregressive (LA) transformer of
the attention mask and convolution mechanism. Thus iLAT can efficiently
synthesize the local image regions by key guidance information. Our iLAT is
evaluated on various locally guided image syntheses, such as pose-guided person
image synthesis and face editing. Both the quantitative and qualitative results
show the efficacy of our model. | [
"cs.CV",
"eess.IV"
] |
Attention-based scene text recognizers have gained huge success, which
leverages a more compact intermediate representation to learn 1d- or 2d-
attention by a RNN-based encoder-decoder architecture. However, such methods
suffer from attention-drift problem because high similarity among encoded
features leads to attention confusion under the RNN-based local attention
mechanism. Moreover, RNN-based methods have low efficiency due to poor
parallelization. To overcome these problems, we propose the MASTER, a
self-attention based scene text recognizer that (1) not only encodes the
input-output attention but also learns self-attention which encodes
feature-feature and target-target relationships inside the encoder and decoder
and (2) learns a more powerful and robust intermediate representation to
spatial distortion, and (3) owns a great training efficiency because of high
training parallelization and a high-speed inference because of an efficient
memory-cache mechanism. Extensive experiments on various benchmarks demonstrate
the superior performance of our MASTER on both regular and irregular scene
text. Pytorch code can be found at https://github.com/wenwenyu/MASTER-pytorch,
and Tensorflow code can be found at https://github.com/jiangxiluning/MASTER-TF. | [
"cs.CV"
] |
Until now, all single level segmentation algorithms except CNN-based ones
lead to over segmentation. And CNN-based segmentation algorithms have their own
problems. To avoid over segmentation, multiple thresholds of criteria are
adopted in region merging process to produce hierarchical segmentation results.
However, there still has extreme over segmentation in the low level of the
hierarchy, and outstanding tiny objects are merged to their large adjacencies
in the high level of the hierarchy. This paper proposes a region-merging-based
image segmentation method that we call it Dam Burst. As a single level
segmentation algorithm, this method avoids over segmentation and retains
details by the same time. It is named because of that it simulates a flooding
from underground destroys dams between water-pools. We treat edge detection
results as strengthening structure of a dam if it is on the dam. To simulate a
flooding from underground, regions are merged by ascending order of the average
gra-dient inside the region. | [
"cs.CV"
] |
Discovering causal structure among a set of variables is a fundamental
problem in many empirical sciences. Traditional score-based casual discovery
methods rely on various local heuristics to search for a Directed Acyclic Graph
(DAG) according to a predefined score function. While these methods, e.g.,
greedy equivalence search, may have attractive results with infinite samples
and certain model assumptions, they are usually less satisfactory in practice
due to finite data and possible violation of assumptions. Motivated by recent
advances in neural combinatorial optimization, we propose to use Reinforcement
Learning (RL) to search for the DAG with the best scoring. Our encoder-decoder
model takes observable data as input and generates graph adjacency matrices
that are used to compute rewards. The reward incorporates both the predefined
score function and two penalty terms for enforcing acyclicity. In contrast with
typical RL applications where the goal is to learn a policy, we use RL as a
search strategy and our final output would be the graph, among all graphs
generated during training, that achieves the best reward. We conduct
experiments on both synthetic and real datasets, and show that the proposed
approach not only has an improved search ability but also allows a flexible
score function under the acyclicity constraint. | [
"cs.LG",
"stat.ML"
] |
A disentangled representation encodes information about the salient factors
of variation in the data independently. Although it is often argued that this
representational format is useful in learning to solve many real-world
down-stream tasks, there is little empirical evidence that supports this claim.
In this paper, we conduct a large-scale study that investigates whether
disentangled representations are more suitable for abstract reasoning tasks.
Using two new tasks similar to Raven's Progressive Matrices, we evaluate the
usefulness of the representations learned by 360 state-of-the-art unsupervised
disentanglement models. Based on these representations, we train 3600 abstract
reasoning models and observe that disentangled representations do in fact lead
to better down-stream performance. In particular, they enable quicker learning
using fewer samples. | [
"cs.LG",
"cs.CV",
"cs.NE",
"stat.ML",
"I.2.6"
] |
Generating videos from text is a challenging task due to its high
computational requirements for training and infinite possible answers for
evaluation. Existing works typically experiment on simple or small datasets,
where the generalization ability is quite limited. In this work, we propose
GODIVA, an open-domain text-to-video pretrained model that can generate videos
from text in an auto-regressive manner using a three-dimensional sparse
attention mechanism. We pretrain our model on Howto100M, a large-scale
text-video dataset that contains more than 136 million text-video pairs.
Experiments show that GODIVA not only can be fine-tuned on downstream video
generation tasks, but also has a good zero-shot capability on unseen texts. We
also propose a new metric called Relative Matching (RM) to automatically
evaluate the video generation quality. Several challenges are listed and
discussed as future work. | [
"cs.CV"
] |
The introduction of inexpensive 3D data acquisition devices has promisingly
facilitated the wide availability and popularity of 3D point cloud, which
attracts more attention to the effective extraction of novel 3D point cloud
descriptors for accuracy of the efficiency of 3D computer vision tasks in
recent years. However, how to develop discriminative and robust feature
descriptors from 3D point cloud remains a challenging task due to their
intrinsic characteristics. In this paper, we give a comprehensively insightful
investigation of the existing 3D point cloud descriptors. These methods can
principally be divided into two categories according to the advancement of
descriptors: hand-crafted based and deep learning-based apporaches, which will
be further discussed from the perspective of elaborate classification, their
advantages, and limitations. Finally, we present the future research direction
of the extraction of 3D point cloud descriptors. | [
"cs.CV"
] |
Deep learning has significantly improved 2D image recognition. Extending into
3D may advance many new applications including autonomous vehicles, virtual and
augmented reality, authoring 3D content, and even improving 2D recognition.
However despite growing interest, 3D deep learning remains relatively
underexplored. We believe that some of this disparity is due to the engineering
challenges involved in 3D deep learning, such as efficiently processing
heterogeneous data and reframing graphics operations to be differentiable. We
address these challenges by introducing PyTorch3D, a library of modular,
efficient, and differentiable operators for 3D deep learning. It includes a
fast, modular differentiable renderer for meshes and point clouds, enabling
analysis-by-synthesis approaches. Compared with other differentiable renderers,
PyTorch3D is more modular and efficient, allowing users to more easily extend
it while also gracefully scaling to large meshes and images. We compare the
PyTorch3D operators and renderer with other implementations and demonstrate
significant speed and memory improvements. We also use PyTorch3D to improve the
state-of-the-art for unsupervised 3D mesh and point cloud prediction from 2D
images on ShapeNet. PyTorch3D is open-source and we hope it will help
accelerate research in 3D deep learning. | [
"cs.CV",
"cs.GR",
"cs.LG"
] |
At present, high-dimensional global optimization problems with time-series
models have received much attention from engineering fields. Since it was
proposed, Bayesian optimization has quickly become a popular and promising
approach for solving global optimization problems. However, the standard
Bayesian optimization algorithm is insufficient to solving the global optimal
solution when the model is high-dimensional. Hence, this paper presents a novel
high dimensional Bayesian optimization algorithm by considering dimension
reduction and different dimension fill-in strategies. Most existing literature
about Bayesian optimization algorithms did not discuss the sampling strategies
to optimize the acquisition function. This study proposed a new sampling method
based on both the multi-armed bandit and random search methods while optimizing
the acquisition function. Besides, based on the time-dependent or
dimension-dependent characteristics of the model, the proposed algorithm can
reduce the dimension evenly. Then, five different dimension fill-in strategies
were discussed and compared in this study. Finally, to increase the final
accuracy of the optimal solution, the proposed algorithm adds a local search
based on a series of Adam-based steps at the final stage. Our computational
experiments demonstrated that the proposed Bayesian optimization algorithm
could achieve reasonable solutions with excellent performances for high
dimensional global optimization problems with a time-series optimal control
model. | [
"cs.LG",
"math.OC",
"stat.AP",
"stat.ME"
] |
Neural networks require careful weight initialization to prevent signals from
exploding or vanishing. Existing initialization schemes solve this problem in
specific cases by assuming that the network has a certain activation function
or topology. It is difficult to derive such weight initialization strategies,
and modern architectures therefore often use these same initialization schemes
even though their assumptions do not hold. This paper introduces AutoInit, a
weight initialization algorithm that automatically adapts to different neural
network architectures. By analytically tracking the mean and variance of
signals as they propagate through the network, AutoInit is able to
appropriately scale the weights at each layer to avoid exploding or vanishing
signals. Experiments demonstrate that AutoInit improves performance of various
convolutional and residual networks across a range of activation function,
dropout, weight decay, learning rate, and normalizer settings. Further, in
neural architecture search and activation function meta-learning, AutoInit
automatically calculates specialized weight initialization strategies for
thousands of unique architectures and hundreds of unique activation functions,
and improves performance in vision, language, tabular, multi-task, and transfer
learning scenarios. AutoInit thus serves as an automatic configuration tool
that makes design of new neural network architectures more robust. The AutoInit
package provides a wrapper around existing TensorFlow models and is available
at https://github.com/cognizant-ai-labs/autoinit. | [
"cs.LG"
] |
In this paper, we address the semantic segmentation task with a deep network
that combines contextual features and spatial information. The proposed Cross
Attention Network is composed of two branches and a Feature Cross Attention
(FCA) module. Specifically, a shallow branch is used to preserve low-level
spatial information and a deep branch is employed to extract high-level
contextual features. Then the FCA module is introduced to combine these two
branches. Different from most existing attention mechanisms, the FCA module
obtains spatial attention map and channel attention map from two branches
separately, and then fuses them. The contextual features are used to provide
global contextual guidance in fused feature maps, and spatial features are used
to refine localizations. The proposed network outperforms other real-time
methods with improved speed on the Cityscapes and CamVid datasets with
lightweight backbones, and achieves state-of-the-art performance with a deep
backbone. | [
"cs.CV"
] |
This paper presents an approach to forecast future presence and location of
human hands and objects. Given an image frame, the goal is to predict what
objects will appear in the future frame (e.g., 5 seconds later) and where they
will be located at, even when they are not visible in the current frame. The
key idea is that (1) an intermediate representation of a convolutional object
recognition model abstracts scene information in its frame and that (2) we can
predict (i.e., regress) such representations corresponding to the future frames
based on that of the current frame. We design a new two-stream convolutional
neural network (CNN) architecture for videos by extending the state-of-the-art
convolutional object detection network, and present a new fully convolutional
regression network for predicting future scene representations. Our experiments
confirm that combining the regressed future representation with our detection
network allows reliable estimation of future hands and objects in videos. We
obtain much higher accuracy compared to the state-of-the-art future object
presence forecast method on a public dataset. | [
"cs.CV"
] |
Intrinsically motivated goal exploration algorithms enable machines to
discover repertoires of policies that produce a diversity of effects in complex
environments. These exploration algorithms have been shown to allow real world
robots to acquire skills such as tool use in high-dimensional continuous state
and action spaces. However, they have so far assumed that self-generated goals
are sampled in a specifically engineered feature space, limiting their
autonomy. In this work, we propose to use deep representation learning
algorithms to learn an adequate goal space. This is a developmental 2-stage
approach: first, in a perceptual learning stage, deep learning algorithms use
passive raw sensor observations of world changes to learn a corresponding
latent space; then goal exploration happens in a second stage by sampling goals
in this latent space. We present experiments where a simulated robot arm
interacts with an object, and we show that exploration algorithms using such
learned representations can match the performance obtained using engineered
representations. | [
"cs.LG",
"cs.AI"
] |
Graph embeddings are a ubiquitous tool for machine learning tasks, such as
node classification and link prediction, on graph-structured data. However,
computing the embeddings for large-scale graphs is prohibitively inefficient
even if we are interested only in a small subset of relevant vertices. To
address this, we present an efficient graph coarsening approach, based on Schur
complements, for computing the embedding of the relevant vertices. We prove
that these embeddings are preserved exactly by the Schur complement graph that
is obtained via Gaussian elimination on the non-relevant vertices. As computing
Schur complements is expensive, we give a nearly-linear time algorithm that
generates a coarsened graph on the relevant vertices that provably matches the
Schur complement in expectation in each iteration. Our experiments involving
prediction tasks on graphs demonstrate that computing embeddings on the
coarsened graph, rather than the entire graph, leads to significant time
savings without sacrificing accuracy. | [
"cs.LG",
"cs.DS",
"stat.ML"
] |
Monocular depth estimation enables 3D perception from a single 2D image, thus
attracting much research attention for years. Almost all methods treat
foreground and background regions ("things and stuff") in an image equally.
However, not all pixels are equal. Depth of foreground objects plays a crucial
role in 3D object recognition and localization. To date how to boost the depth
prediction accuracy of foreground objects is rarely discussed. In this paper,
we first analyse the data distributions and interaction of foreground and
background, then propose the foreground-background separated monocular depth
estimation (ForeSeE) method, to estimate the foreground depth and background
depth using separate optimization objectives and depth decoders. Our method
significantly improves the depth estimation performance on foreground objects.
Applying ForeSeE to 3D object detection, we achieve 7.5 AP gains and set new
state-of-the-art results among other monocular methods. Code will be available
at: https://github.com/WXinlong/ForeSeE. | [
"cs.CV"
] |
We propose Chirality Nets, a family of deep nets that is equivariant to the
"chirality transform," i.e., the transformation to create a chiral pair.
Through parameter sharing, odd and even symmetry, we propose and prove variants
of standard building blocks of deep nets that satisfy the equivariance
property, including fully connected layers, convolutional layers,
batch-normalization, and LSTM/GRU cells. The proposed layers lead to a more
data efficient representation and a reduction in computation by exploiting
symmetry. We evaluate chirality nets on the task of human pose regression,
which naturally exploits the left/right mirroring of the human body. We study
three pose regression tasks: 3D pose estimation from video, 2D pose
forecasting, and skeleton based activity recognition. Our approach
achieves/matches state-of-the-art results, with more significant gains on small
datasets and limited-data settings. | [
"cs.CV",
"cs.LG"
] |
We study reinforcement learning of chatbots with recurrent neural network
architectures when the rewards are noisy and expensive to obtain. For instance,
a chatbot used in automated customer service support can be scored by quality
assurance agents, but this process can be expensive, time consuming and noisy.
Previous reinforcement learning work for natural language processing uses
on-policy updates and/or is designed for on-line learning settings. We
demonstrate empirically that such strategies are not appropriate for this
setting and develop an off-policy batch policy gradient method (BPG). We
demonstrate the efficacy of our method via a series of synthetic experiments
and an Amazon Mechanical Turk experiment on a restaurant recommendations
dataset. | [
"stat.ML",
"cs.LG"
] |
This paper proposes a generative ScatterNet hybrid deep learning (G-SHDL)
network for semantic image segmentation. The proposed generative architecture
is able to train rapidly from relatively small labeled datasets using the
introduced structural priors. In addition, the number of filters in each layer
of the architecture is optimized resulting in a computationally efficient
architecture. The G-SHDL network produces state-of-the-art classification
performance against unsupervised and semi-supervised learning on two image
datasets. Advantages of the G-SHDL network over supervised methods are
demonstrated with experiments performed on training datasets of reduced size. | [
"cs.CV"
] |
There has been increasing interest in building deep hierarchy-aware
classifiers that aim to quantify and reduce the severity of mistakes, and not
just reduce the number of errors. The idea is to exploit the label hierarchy
(e.g., the WordNet ontology) and consider graph distances as a proxy for
mistake severity. Surprisingly, on examining mistake-severity distributions of
the top-1 prediction, we find that current state-of-the-art hierarchy-aware
deep classifiers do not always show practical improvement over the standard
cross-entropy baseline in making better mistakes. The reason for the reduction
in average mistake-severity can be attributed to the increase in low-severity
mistakes, which may also explain the noticeable drop in their accuracy. To this
end, we use the classical Conditional Risk Minimization (CRM) framework for
hierarchy-aware classification. Given a cost matrix and a reliable estimate of
likelihoods (obtained from a trained network), CRM simply amends mistakes at
inference time; it needs no extra hyperparameters and requires adding just a
few lines of code to the standard cross-entropy baseline. It significantly
outperforms the state-of-the-art and consistently obtains large reductions in
the average hierarchical distance of top-$k$ predictions across datasets, with
very little loss in accuracy. CRM, because of its simplicity, can be used with
any off-the-shelf trained model that provides reliable likelihood estimates. | [
"cs.LG",
"cs.CV"
] |
The recently-proposed Perceiver model obtains good results on several domains
(images, audio, multimodal, point clouds) while scaling linearly in compute and
memory with the input size. While the Perceiver supports many kinds of inputs,
it can only produce very simple outputs such as class scores. Perceiver IO
overcomes this limitation without sacrificing the original's appealing
properties by learning to flexibly query the model's latent space to produce
outputs of arbitrary size and semantics. Perceiver IO still decouples model
depth from data size and still scales linearly with data size, but now with
respect to both input and output sizes. The full Perceiver IO model achieves
strong results on tasks with highly structured output spaces, such as natural
language and visual understanding, StarCraft II, and multi-task and multi-modal
domains. As highlights, Perceiver IO matches a Transformer-based BERT baseline
on the GLUE language benchmark without the need for input tokenization and
achieves state-of-the-art performance on Sintel optical flow estimation. | [
"cs.LG",
"cs.CL",
"cs.CV",
"cs.SD",
"eess.AS"
] |
Variational Auto-Encoders (VAEs) have shown great potential in the
unsupervised learning of data distributions. An VAE trained on normal images is
expected to only be able to reconstruct normal images, allowing the
localization of anomalous pixels in an image via manipulating information
within the VAE ELBO loss. The ELBO consists of KL divergence loss (image-wise)
and reconstruction loss (pixel-wise). It is natural and straightforward to use
the later as the predictor. However, usually local anomaly added to a normal
image can deteriorate the whole reconstructed image, causing segmentation using
only naive pixel errors not accurate. Energy based projection was proposed to
increase the reconstruction accuracy of normal regions/pixels, which achieved
the state-of-the-art localization accuracy on simple natural images. Another
possible predictors are ELBO and its components gradients with respect to each
pixels. Previous work claimed that KL gradient is a robust predictor. In this
paper, we argue that the energy based projection in medical imaging is not as
useful as on natural images. Moreover, we observe that the robustness of KL
gradient predictor totally depends on the setting of the VAE and dataset. We
also explored the effect of the weight of KL loss within beta-VAE and predictor
ensemble in anomaly localization. | [
"cs.CV",
"cs.LG"
] |
Can we learn how to explore unknown spaces efficiently? To answer this
question, we study the problem of Online Graph Exploration, the online version
of the Traveling Salesperson Problem. We reformulate graph exploration as a
reinforcement learning problem and apply Direct Future Prediction (Dosovitskiy
and Koltun, 2017) to solve it. As the graph is discovered online, the
corresponding Markov Decision Process entails a dynamic state space, namely the
observable graph and a dynamic action space, namely the nodes forming the
graph's frontier. To the best of our knowledge, this is the first attempt to
solve online graph exploration in a data-driven way. We conduct experiments on
six data sets of procedurally generated graphs and three real city road
networks. We demonstrate that our agent can learn strategies superior to many
well known graph traversal algorithms, confirming that exploration can be
learned. | [
"cs.LG",
"cs.AI"
] |
In this paper, we propose a novel deep framework for part-level semantic
parsing of freehand sketches, which makes three main contributions that are
experimentally shown to have substantial practical merit. First, we propose a
homogeneous transformation method to address the problem of domain adaptation.
For the task of sketch parsing, there is no available data of labeled freehand
sketches that can be directly used for model training. An alternative solution
is to learn from datasets of real image parsing, while the domain adaptation is
an inevitable problem. Unlike existing methods that utilize the edge maps of
real images to approximate freehand sketches, the proposed homogeneous
transformation method transforms the data from domains of real images and
freehand sketches into a homogeneous space to minimize the semantic gap.
Second, we design a soft-weighted loss function as guidance for the training
process, which gives attention to both the ambiguous label boundary and class
imbalance. Third, we present a staged learning strategy to improve the parsing
performance of the trained model, which takes advantage of the shared
information and specific characteristic from different sketch categories.
Extensive experimental results demonstrate the effectiveness of the above three
methods. Specifically, to evaluate the generalization ability of our
homogeneous transformation method, additional experiments for the task of
sketch-based image retrieval are conducted on the QMUL FG-SBIR dataset.
Finally, by integrating the proposed three methods into a unified framework of
deep semantic sketch parsing (DeepSSP), we achieve the state-of-the-art on the
public SketchParse dataset. | [
"cs.CV"
] |
Though 3D object detection from point clouds has achieved rapid progress in
recent years, the lack of flexible and high-performance proposal refinement
remains a great hurdle for existing state-of-the-art two-stage detectors.
Previous works on refining 3D proposals have relied on human-designed
components such as keypoints sampling, set abstraction and multi-scale feature
fusion to produce powerful 3D object representations. Such methods, however,
have limited ability to capture rich contextual dependencies among points. In
this paper, we leverage the high-quality region proposal network and a
Channel-wise Transformer architecture to constitute our two-stage 3D object
detection framework (CT3D) with minimal hand-crafted design. The proposed CT3D
simultaneously performs proposal-aware embedding and channel-wise context
aggregation for the point features within each proposal. Specifically, CT3D
uses proposal's keypoints for spatial contextual modelling and learns attention
propagation in the encoding module, mapping the proposal to point embeddings.
Next, a new channel-wise decoding module enriches the query-key interaction via
channel-wise re-weighting to effectively merge multi-level contexts, which
contributes to more accurate object predictions. Extensive experiments
demonstrate that our CT3D method has superior performance and excellent
scalability. Remarkably, CT3D achieves the AP of 81.77% in the moderate car
category on the KITTI test 3D detection benchmark, outperforms state-of-the-art
3D detectors. | [
"cs.CV"
] |
Cryogenic electron microscopy (cryo-EM) has become an enabling technology in
drug discovery and in understanding molecular bases of disease by producing
near-atomic resolution (less than 0.4 nm) 3D reconstructions of biological
macromolecules. The imaging process required for 3D reconstructions involves a
highly iterative and empirical screening process, starting with the acquisition
of low magnification images of the cryo-EM grids. These images are inspected
for squares that are likely to contain useful molecular signals. Potentially
useful squares within the grid are then imaged at progressively higher
magnifications, with the goal of identifying sub-micron areas within circular
holes (bounded by the squares) for imaging at high magnification. This arduous,
multi-step data acquisition process represents a bottleneck for obtaining a
high throughput data collection. Here, we focus on automating the early
decision making for the microscope operator, scoring low magnification images
of squares, and proposing the first deep learning framework, XCryoNet, for
automated cryo-EM grid screening. XCryoNet is a semi-supervised,
attention-guided deep learning approach that provides explainable scoring of
automatically extracted square images using limited amounts of labeled data.
Results show up to 8% and 37% improvements over a fully supervised and a
no-attention solution, respectively, when labeled data is scarce. | [
"cs.CV",
"cs.LG"
] |
We present a factorized hierarchical variational autoencoder, which learns
disentangled and interpretable representations from sequential data without
supervision. Specifically, we exploit the multi-scale nature of information in
sequential data by formulating it explicitly within a factorized hierarchical
graphical model that imposes sequence-dependent priors and sequence-independent
priors to different sets of latent variables. The model is evaluated on two
speech corpora to demonstrate, qualitatively, its ability to transform speakers
or linguistic content by manipulating different sets of latent variables; and
quantitatively, its ability to outperform an i-vector baseline for speaker
verification and reduce the word error rate by as much as 35% in mismatched
train/test scenarios for automatic speech recognition tasks. | [
"cs.LG",
"cs.CL",
"cs.SD",
"eess.AS",
"stat.ML"
] |
Most existing RGB-D salient object detection (SOD) methods focus on the
foreground region when utilizing the depth images. However, the background also
provides important information in traditional SOD methods for promising
performance. To better explore salient information in both foreground and
background regions, this paper proposes a Bilateral Attention Network (BiANet)
for the RGB-D SOD task. Specifically, we introduce a Bilateral Attention Module
(BAM) with a complementary attention mechanism: foreground-first (FF) attention
and background-first (BF) attention. The FF attention focuses on the foreground
region with a gradual refinement style, while the BF one recovers potentially
useful salient information in the background region. Benefitted from the
proposed BAM module, our BiANet can capture more meaningful foreground and
background cues, and shift more attention to refining the uncertain details
between foreground and background regions. Additionally, we extend our BAM by
leveraging the multi-scale techniques for better SOD performance. Extensive
experiments on six benchmark datasets demonstrate that our BiANet outperforms
other state-of-the-art RGB-D SOD methods in terms of objective metrics and
subjective visual comparison. Our BiANet can run up to 80fps on $224\times224$
RGB-D images, with an NVIDIA GeForce RTX 2080Ti GPU. Comprehensive ablation
studies also validate our contributions. | [
"cs.CV"
] |
Vision Transformers (ViT) have been shown to attain highly competitive
performance for a wide range of vision applications, such as image
classification, object detection and semantic image segmentation. In comparison
to convolutional neural networks, the Vision Transformer's weaker inductive
bias is generally found to cause an increased reliance on model regularization
or data augmentation (``AugReg'' for short) when training on smaller training
datasets. We conduct a systematic empirical study in order to better understand
the interplay between the amount of training data, AugReg, model size and
compute budget. As one result of this study we find that the combination of
increased compute and AugReg can yield models with the same performance as
models trained on an order of magnitude more training data: we train ViT models
of various sizes on the public ImageNet-21k dataset which either match or
outperform their counterparts trained on the larger, but not publicly available
JFT-300M dataset. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), are
ideally suited for real-time motion analysis. The unique properties encompassed
in the readings of such sensors provide high temporal resolution, superior
sensitivity to light and low latency. These properties provide the grounds to
estimate motion extremely reliably in the most sophisticated scenarios but they
come at a price - modern event-based vision sensors have extremely low
resolution and produce a lot of noise. Moreover, the asynchronous nature of the
event stream calls for novel algorithms.
This paper presents a new, efficient approach to object tracking with
asynchronous cameras. We present a novel event stream representation which
enables us to utilize information about the dynamic (temporal) component of the
event stream, and not only the spatial component, at every moment of time. This
is done by approximating the 3D geometry of the event stream with a parametric
model; as a result, the algorithm is capable of producing the
motion-compensated event stream (effectively approximating egomotion), and
without using any form of external sensors in extremely low-light and noisy
conditions without any form of feature tracking or explicit optical flow
computation. We demonstrate our framework on the task of independent motion
detection and tracking, where we use the temporal model inconsistencies to
locate differently moving objects in challenging situations of very fast
motion. | [
"cs.CV"
] |
We designed a gangue sorting system,and built a convolutional neural network
model based on AlexNet. Data enhancement and transfer learning are used to
solve the problem which the convolution neural network has insufficient
training data in the training stage. An object detection and region clipping
algorithm is proposed to adjust the training image data to the optimum size.
Compared with traditional neural network and SVM algorithm, this algorithm has
higher recognition rate for coal and coal gangue, and provides important
reference for identification and separation of coal and gangue. | [
"cs.CV"
] |
A key element of any machine learning algorithm is the use of a function that
measures the dis/similarity between data points. Given a task, such a function
can be optimized with a metric learning algorithm. Although this research field
has received a lot of attention during the past decade, very few approaches
have focused on learning a metric in an imbalanced scenario where the number of
positive examples is much smaller than the negatives. Here, we address this
challenging task by designing a new Mahalanobis metric learning algorithm (IML)
which deals with class imbalance. The empirical study performed shows the
efficiency of IML. | [
"stat.ML",
"cs.LG"
] |
Automatic License Plate Recognition (ALPR) is a challenging problem to the
research community due to its potential applicability in the diverse
geographical condition over the globe with varying license plate parameters.
Any ALPR system includes three main modules, viz. localization of the license
plate, segmentation of the characters therein and recognition of the segmented
characters. In real life applications where the images are captured over days
and nights in an outdoor environment with varying lighting and weather
conditions, varying pollution level and wind turbulences, localization,
segmentation and recognition become challenging tasks. The tasks become more
complex if the license plate is not in conformity with the standards laid by
corresponding Motor Vehicles Department in terms of various features, e.g. area
and aspect ratio of the license plate, background color, foreground color,
shape, number of lines, font face/ size of characters, spacing between
characters etc. Besides, license plates are often dirty or broken or having
scratches or bent or tilted at its position. All these add to the challenges in
developing an effective ALPR system. | [
"cs.CV"
] |
Object detection from RGB images is a long-standing problem in image
processing and computer vision. It has applications in various domains
including robotics, surveillance, human-computer interaction, and medical
diagnosis. With the availability of low cost 3D scanners, a large number of
RGB-D object detection approaches have been proposed in the past years. This
chapter provides a comprehensive survey of the recent developments in this
field. We structure the chapter into two parts; the focus of the first part is
on techniques that are based on hand-crafted features combined with machine
learning algorithms. The focus of the second part is on the more recent work,
which is based on deep learning. Deep learning techniques, coupled with the
availability of large training datasets, have now revolutionized the field of
computer vision, including RGB-D object detection, achieving an unprecedented
level of performance. We survey the key contributions, summarize the most
commonly used pipelines, discuss their benefits and limitations, and highlight
some important directions for future research. | [
"cs.CV",
"cs.LG"
] |
Differentiable forest is an ensemble of decision trees with full
differentiability. Its simple tree structure is easy to use and explain. With
full differentiability, it would be trained in the end-to-end learning
framework with gradient-based optimization method. In this paper, we propose
tree attention block(TAB) in the framework of differentiable forest. TAB block
has two operations, squeeze and regulate. The squeeze operation would extract
the characteristic of each tree. The regulate operation would learn nonlinear
relations between these trees. So TAB block would learn the importance of each
tree and adjust its weight to improve accuracy. Our experiment on large tabular
dataset shows attention augmented differentiable forest would get comparable
accuracy with gradient boosted decision trees(GBDT), which is the
state-of-the-art algorithm for tabular datasets. And on some datasets, our
model has higher accuracy than best GBDT libs (LightGBM, Catboost, and
XGBoost). Differentiable forest model supports batch training and batch size is
much smaller than the size of training set. So on larger data sets, its memory
usage is much lower than GBDT model. The source codes are available at
https://github.com/closest-git/QuantumForest. | [
"cs.LG",
"stat.ML"
] |
In supervised learning, smoothing label or prediction distribution in neural
network training has been proven useful in preventing the model from being
over-confident, and is crucial for learning more robust visual representations.
This observation motivates us to explore ways to make predictions flattened in
unsupervised learning. Considering that human-annotated labels are not adopted
in unsupervised learning, we introduce a straightforward approach to perturb
input image space in order to soften the output prediction space indirectly,
meanwhile, assigning new label values in the unsupervised frameworks
accordingly. Despite its conceptual simplicity, we show empirically that with
the simple solution -- Unsupervised image mixtures (Un-Mix), we can learn more
robust visual representations from the transformed input. Extensive experiments
are conducted on CIFAR-10, CIFAR-100, STL-10, Tiny ImageNet and standard
ImageNet with popular unsupervised methods SimCLR, BYOL, MoCo V1&V2, etc. Our
proposed image mixture and label assignment strategy can obtain consistent
improvement by 1~3% following exactly the same hyperparameters and training
procedures of the base methods. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
This paper presents a novel method for structural data recognition using a
large number of graph models. In general, prevalent methods for structural data
recognition have two shortcomings: 1) Only a single model is used to capture
structural variation. 2) Naive recognition methods are used, such as the
nearest neighbor method. In this paper, we propose strengthening the
recognition performance of these models as well as their ability to capture
structural variation. The proposed method constructs a large number of graph
models and trains decision trees using the models. This paper makes two main
contributions. The first is a novel graph model that can quickly perform
calculations, which allows us to construct several models in a feasible amount
of time. The second contribution is a novel approach to structural data
recognition: graph model boosting. Comprehensive structural variations can be
captured with a large number of graph models constructed in a boosting
framework, and a sophisticated classifier can be formed by aggregating the
decision trees. Consequently, we can carry out structural data recognition with
powerful recognition capability in the face of comprehensive structural
variation. The experiments shows that the proposed method achieves impressive
results and outperforms existing methods on datasets of IAM graph database
repository. | [
"cs.LG",
"stat.ML"
] |
Differentiable image sampling in the form of backward warping has seen broad
adoption in tasks like depth estimation and optical flow prediction. In
contrast, how to perform forward warping has seen less attention, partly due to
additional challenges such as resolving the conflict of mapping multiple pixels
to the same target location in a differentiable way. We propose softmax
splatting to address this paradigm shift and show its effectiveness on the
application of frame interpolation. Specifically, given two input frames, we
forward-warp the frames and their feature pyramid representations based on an
optical flow estimate using softmax splatting. In doing so, the softmax
splatting seamlessly handles cases where multiple source pixels map to the same
target location. We then use a synthesis network to predict the interpolation
result from the warped representations. Our softmax splatting allows us to not
only interpolate frames at an arbitrary time but also to fine tune the feature
pyramid and the optical flow. We show that our synthesis approach, empowered by
softmax splatting, achieves new state-of-the-art results for video frame
interpolation. | [
"cs.CV"
] |
As more researchers have become aware of and passionate about algorithmic
fairness, there has been an explosion in papers laying out new metrics,
suggesting algorithms to address issues, and calling attention to issues in
existing applications of machine learning. This research has greatly expanded
our understanding of the concerns and challenges in deploying machine learning,
but there has been much less work in seeing how the rubber meets the road.
In this paper we provide a case-study on the application of fairness in
machine learning research to a production classification system, and offer new
insights in how to measure and address algorithmic fairness issues. We discuss
open questions in implementing equality of opportunity and describe our
fairness metric, conditional equality, that takes into account distributional
differences. Further, we provide a new approach to improve on the fairness
metric during model training and demonstrate its efficacy in improving
performance for a real-world product | [
"cs.LG",
"cs.AI",
"cs.CY",
"stat.ML"
] |
Prior works on formalizing explanations of a graph neural network (GNN) focus
on a single use case - to preserve the prediction results through identifying
important edges and nodes. In this paper, we develop a multi-purpose
interpretation framework by acquiring a mask that indicates topology
perturbations of the input graphs. We pack the framework into an interactive
visualization system (GNNViz) which can fulfill multiple purposes:
Preserve,Promote, or Attack GNN's predictions. We illustrate our approach's
novelty and effectiveness with three case studies: First, GNNViz can assist non
expert users to easily explore the relationship between graph topology and
GNN's decision (Preserve), or to manipulate the prediction (Promote or Attack)
for an image classification task on MS-COCO; Second, on the Pokec social
network dataset, our framework can uncover unfairness and demographic biases;
Lastly, it compares with state-of-the-art GNN explainer baseline on a synthetic
dataset. | [
"cs.LG"
] |
Egocentric activity recognition in first-person videos has an increasing
importance with a variety of applications such as lifelogging, summarization,
assisted-living and activity tracking. Existing methods for this task are based
on interpretation of various sensor information using pre-determined weights
for each feature. In this work, we propose a new framework for egocentric
activity recognition problem based on combining audio-visual features with
multi-kernel learning (MKL) and multi-kernel boosting (MKBoost). For that
purpose, firstly grid optical-flow, virtual-inertia feature, log-covariance,
cuboid are extracted from the video. The audio signal is characterized using a
"supervector", obtained based on Gaussian mixture modelling of frame-level
features, followed by a maximum a-posteriori adaptation. Then, the extracted
multi-modal features are adaptively fused by MKL classifiers in which both the
feature and kernel selection/weighing and recognition tasks are performed
together. The proposed framework was evaluated on a number of egocentric
datasets. The results showed that using multi-modal features with MKL
outperforms the existing methods. | [
"cs.CV"
] |
We extend the framework of variational autoencoders to represent
transformations explicitly in the latent space. In the family of hierarchical
graphical models that emerges, the latent space is populated by higher order
objects that are inferred jointly with the latent representations they act on.
To explicitly demonstrate the effect of these higher order objects, we show
that the inferred latent transformations reflect interpretable properties in
the observation space. Furthermore, the model is structured in such a way that
in the absence of transformations, we can run inference and obtain generative
capabilities comparable with standard variational autoencoders. Finally,
utilizing the trained encoder, we outperform the baselines by a wide margin on
a challenging out-of-distribution classification task. | [
"cs.LG",
"stat.ML"
] |
Unsupervised domain adaptation (UDA) for person re-identification is
challenging because of the huge gap between the source and target domain. A
typical self-training method is to use pseudo-labels generated by clustering
algorithms to iteratively optimize the model on the target domain. However, a
drawback to this is that noisy pseudo-labels generally cause trouble in
learning. To address this problem, a mutual learning method by dual networks
has been developed to produce reliable soft labels. However, as the two neural
networks gradually converge, their complementarity is weakened and they likely
become biased towards the same kind of noise. This paper proposes a novel
light-weight module, the Attentive WaveBlock (AWB), which can be integrated
into the dual networks of mutual learning to enhance the complementarity and
further depress noise in the pseudo-labels. Specifically, we first introduce a
parameter-free module, the WaveBlock, which creates a difference between
features learned by two networks by waving blocks of feature maps differently.
Then, an attention mechanism is leveraged to enlarge the difference created and
discover more complementary features. Furthermore, two kinds of combination
strategies, i.e. pre-attention and post-attention, are explored. Experiments
demonstrate that the proposed method achieves state-of-the-art performance with
significant improvements on multiple UDA person re-identification tasks. We
also prove the generality of the proposed method by applying it to vehicle
re-identification and image classification tasks. Our codes and models are
available at https://github.com/WangWenhao0716/Attentive-WaveBlock. | [
"cs.CV"
] |
Existing methods for image captioning are usually trained by cross entropy
loss, which leads to exposure bias and the inconsistency between the optimizing
function and evaluation metrics. Recently it has been shown that these two
issues can be addressed by incorporating techniques from reinforcement
learning, where one of the popular techniques is the advantage actor-critic
algorithm that calculates per-token advantage by estimating state value with a
parametrized estimator at the cost of introducing estimation bias. In this
paper, we estimate state value without using a parametrized value estimator.
With the properties of image captioning, namely, the deterministic state
transition function and the sparse reward, state value is equivalent to its
preceding state-action value, and we reformulate advantage function by simply
replacing the former with the latter. Moreover, the reformulated advantage is
extended to n-step, which can generally increase the absolute value of the mean
of reformulated advantage while lowering variance. Then two kinds of rollout
are adopted to estimate state-action value, which we call self-critical n-step
training. Empirically we find that our method can obtain better performance
compared to the state-of-the-art methods that use the sequence level advantage
and parametrized estimator respectively on the widely used MSCOCO benchmark. | [
"cs.CV",
"cs.CL",
"cs.LG"
] |
Reinforcement learning has gained wide popularity as a technique for
simulation-driven approximate dynamic programming. A less known aspect is that
the very reasons that make it effective in dynamic programming can also be
leveraged for using it for distributed schemes for certain matrix computations
involving non-negative matrices. In this spirit, we propose a reinforcement
learning algorithm for PageRank computation that is fashioned after analogous
schemes for approximate dynamic programming. The algorithm has the advantage of
ease of distributed implementation and more importantly, of being model-free,
i.e., not dependent on any specific assumptions about the transition
probabilities in the random web-surfer model. We analyze its convergence and
finite time behavior and present some supporting numerical experiments. | [
"cs.LG",
"cs.SI",
"stat.ML"
] |
Current state-of-the-art self-supervised learning methods for graph neural
networks (GNNs) are based on contrastive learning. As such, they heavily depend
on the construction of augmentations and negative examples. For example, on the
standard PPI benchmark, increasing the number of negative pairs improves
performance, thereby requiring computation and memory cost quadratic in the
number of nodes to achieve peak performance. Inspired by BYOL, a recently
introduced method for self-supervised learning that does not require negative
pairs, we present Bootstrapped Graph Latents, BGRL, a self-supervised graph
representation method that gets rid of this potentially quadratic bottleneck.
BGRL outperforms or matches the previous unsupervised state-of-the-art results
on several established benchmark datasets. Moreover, it enables the effective
usage of graph attentional (GAT) encoders, allowing us to further improve the
state of the art. In particular on the PPI dataset, using GAT as an encoder we
achieve state-of-the-art 70.49% Micro-F1, using the linear evaluation protocol.
On all other datasets under consideration, our model is competitive with the
equivalent supervised GNN results, often exceeding them. | [
"cs.LG",
"cs.SI",
"stat.ML"
] |
Predicting future video frames is extremely challenging, as there are many
factors of variation that make up the dynamics of how frames change through
time. Previously proposed solutions require complex inductive biases inside
network architectures with highly specialized computation, including
segmentation masks, optical flow, and foreground and background separation. In
this work, we question if such handcrafted architectures are necessary and
instead propose a different approach: finding minimal inductive bias for video
prediction while maximizing network capacity. We investigate this question by
performing the first large-scale empirical study and demonstrate
state-of-the-art performance by learning large models on three different
datasets: one for modeling object interactions, one for modeling human motion,
and one for modeling car driving. | [
"cs.CV"
] |
While activity recognition from inertial sensors holds potential for mobile
health, differences in sensing platforms and user movement patterns cause
performance degradation. Aiming to address these challenges, we propose a
transfer learning framework, TransFall, for sensor-based activity recognition.
TransFall's design contains a two-tier data transformation, a label estimation
layer, and a model generation layer to recognize activities for the new
scenario. We validate TransFall analytically and empirically. | [
"cs.LG",
"cs.HC",
"stat.ML"
] |
In this paper, we introduce a new reinforcement learning (RL) based neural
architecture search (NAS) methodology for effective and efficient generative
adversarial network (GAN) architecture search. The key idea is to formulate the
GAN architecture search problem as a Markov decision process (MDP) for smoother
architecture sampling, which enables a more effective RL-based search algorithm
by targeting the potential global optimal architecture. To improve efficiency,
we exploit an off-policy GAN architecture search algorithm that makes efficient
use of the samples generated by previous policies. Evaluation on two standard
benchmark datasets (i.e., CIFAR-10 and STL-10) demonstrates that the proposed
method is able to discover highly competitive architectures for generally
better image generation results with a considerably reduced computational
burden: 7 GPU hours. Our code is available at
https://github.com/Yuantian013/E2GAN. | [
"cs.CV"
] |
Crowdsourcing has attracted much attention for its convenience to collect
labels from non-expert workers instead of experts. However, due to the high
level of noise from the non-experts, an aggregation model that learns the true
label by incorporating the source credibility is required. In this paper, we
propose a novel framework based on graph neural networks for aggregating crowd
labels. We construct a heterogeneous graph between workers and tasks and derive
a new graph neural network to learn the representations of nodes and the true
labels. Besides, we exploit the unknown latent interaction between the same
type of nodes (workers or tasks) by adding a homogeneous attention layer in the
graph neural networks. Experimental results on 13 real-world datasets show
superior performance over state-of-the-art models. | [
"cs.LG",
"cs.HC"
] |
Empirical scoring functions based on either molecular force fields or
cheminformatics descriptors are widely used, in conjunction with molecular
docking, during the early stages of drug discovery to predict potency and
binding affinity of a drug-like molecule to a given target. These models
require expert-level knowledge of physical chemistry and biology to be encoded
as hand-tuned parameters or features rather than allowing the underlying model
to select features in a data-driven procedure. Here, we develop a general
3-dimensional spatial convolution operation for learning atomic-level chemical
interactions directly from atomic coordinates and demonstrate its application
to structure-based bioactivity prediction. The atomic convolutional neural
network is trained to predict the experimentally determined binding affinity of
a protein-ligand complex by direct calculation of the energy associated with
the complex, protein, and ligand given the crystal structure of the binding
pose. Non-covalent interactions present in the complex that are absent in the
protein-ligand sub-structures are identified and the model learns the
interaction strength associated with these features. We test our model by
predicting the binding free energy of a subset of protein-ligand complexes
found in the PDBBind dataset and compare with state-of-the-art cheminformatics
and machine learning-based approaches. We find that all methods achieve
experimental accuracy and that atomic convolutional networks either outperform
or perform competitively with the cheminformatics based methods. Unlike all
previous protein-ligand prediction systems, atomic convolutional networks are
end-to-end and fully-differentiable. They represent a new data-driven,
physics-based deep learning model paradigm that offers a strong foundation for
future improvements in structure-based bioactivity prediction. | [
"cs.LG",
"physics.chem-ph",
"stat.ML"
] |
We propose a novel reference-based video colorization framework with
spatiotemporal correspondence. Reference-based methods colorize grayscale
frames referencing a user input color frame. Existing methods suffer from the
color leakage between objects and the emergence of average colors, derived from
non-local semantic correspondence in space. To address this issue, we warp
colors only from the regions on the reference frame restricted by
correspondence in time. We propagate masks as temporal correspondences, using
two complementary tracking approaches: off-the-shelf instance tracking for high
performance segmentation, and newly proposed dense tracking to track various
types of objects. By restricting temporally-related regions for referencing
colors, our approach propagates faithful colors throughout the video.
Experiments demonstrate that our method outperforms state-of-the-art methods
quantitatively and qualitatively. | [
"cs.CV"
] |
Student procrastination and cramming for deadlines are major challenges in
online learning environments, with negative educational and well-being side
effects. Modeling student activities in continuous time and predicting their
next study time are important problems that can help in creating personalized
timely interventions to mitigate these challenges. However, previous attempts
on dynamic modeling of student procrastination suffer from major issues: they
are unable to predict the next activity times, cannot deal with missing
activity history, are not personalized, and disregard important course
properties, such as assignment deadlines, that are essential in explaining the
cramming behavior. To resolve these problems, we introduce a new personalized
stimuli-sensitive Hawkes process model (SSHP), by jointly modeling all
student-assignment pairs and utilizing their similarities, to predict students'
next activity times even when there are no historical observations. Unlike
regular point processes that assume a constant external triggering effect from
the environment, we model three dynamic types of external stimuli, according to
assignment availabilities, assignment deadlines, and each student's time
management habits. Our experiments on two synthetic datasets and two real-world
datasets show a superior performance of future activity prediction, comparing
with state-of-the-art models. Moreover, we show that our model achieves a
flexible and accurate parameterization of activity intensities in students. | [
"cs.LG",
"cs.CY"
] |
Molecular property prediction is gaining increasing attention due to its
diverse applications. One task of particular interests and importance is to
predict quantum chemical properties without 3D equilibrium structures. This is
practically favorable since obtaining 3D equilibrium structures requires
extremely expensive calculations. In this work, we design a deep graph neural
network to predict quantum properties by directly learning from 2D molecular
graphs. In addition, we propose a 3D graph neural network to learn from
low-cost conformer sets, which can be obtained with open-source tools using an
affordable budget. We employ our methods to participate in the 2021 KDD Cup on
OGB Large-Scale Challenge (OGB-LSC), which aims to predict the HOMO-LUMO energy
gap of molecules. Final evaluation results reveal that we are one of the
winners with a mean absolute error of 0.1235 on the holdout test set. Our
implementation is available as part of the MoleculeX package
(https://github.com/divelab/MoleculeX). | [
"cs.LG"
] |
This paper considers object detection and 3D estimation using an FMCW radar.
The state-of-the-art deep learning framework is employed instead of using
traditional signal processing. In preparing the radar training data, the ground
truth of an object orientation in 3D space is provided by conducting image
analysis, of which the images are obtained through a coupled camera to the
radar device. To ensure successful training of a fully convolutional network
(FCN), we propose a normalization method, which is found to be essential to be
applied to the radar signal before feeding into the neural network. The system
after proper training is able to first detect the presence of an object in an
environment. If it does, the system then further produces an estimation of its
3D position. Experimental results show that the proposed system can be
successfully trained and employed for detecting a car and further estimating
its 3D position in a noisy environment. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
Assessment of mental workload in real-world conditions is key to ensure the
performance of workers executing tasks that demand sustained attention.
Previous literature has employed electroencephalography (EEG) to this end
despite having observed that EEG correlates of mental workload vary across
subjects and physical strain, thus making it difficult to devise models capable
of simultaneously presenting reliable performance across users. Domain
adaptation consists of a set of strategies that aim at allowing for improving
machine learning systems performance on unseen data at training time. Such
methods, however, might rely on assumptions over the considered data
distributions, which typically do not hold for applications of EEG data.
Motivated by this observation, in this work we propose a strategy to estimate
two types of discrepancies between multiple data distributions, namely marginal
and conditional shifts, observed on data collected from different subjects.
Besides shedding light on the assumptions that hold for a particular dataset,
the estimates of statistical shifts obtained with the proposed approach can be
used for investigating other aspects of a machine learning pipeline, such as
quantitatively assessing the effectiveness of domain adaptation strategies. In
particular, we consider EEG data collected from individuals performing mental
tasks while running on a treadmill and pedaling on a stationary bike and
explore the effects of different normalization strategies commonly used to
mitigate cross-subject variability. We show the effects that different
normalization schemes have on statistical shifts and their relationship with
the accuracy of mental workload prediction as assessed on unseen participants
at training time. | [
"cs.LG",
"eess.SP",
"stat.ML"
] |
We propose an Auto-Parsing Network (APN) to discover and exploit the input
data's hidden tree structures for improving the effectiveness of the
Transformer-based vision-language systems. Specifically, we impose a
Probabilistic Graphical Model (PGM) parameterized by the attention operations
on each self-attention layer to incorporate sparse assumption. We use this PGM
to softly segment an input sequence into a few clusters where each cluster can
be treated as the parent of the inside entities. By stacking these PGM
constrained self-attention layers, the clusters in a lower layer compose into a
new sequence, and the PGM in a higher layer will further segment this sequence.
Iteratively, a sparse tree can be implicitly parsed, and this tree's
hierarchical knowledge is incorporated into the transformed embeddings, which
can be used for solving the target vision-language tasks. Specifically, we
showcase that our APN can strengthen Transformer based networks in two major
vision-language tasks: Captioning and Visual Question Answering. Also, a PGM
probability-based parsing algorithm is developed by which we can discover what
the hidden structure of input is during the inference. | [
"cs.CV"
] |
We address the problem of video representation learning without
human-annotated labels. While previous efforts address the problem by designing
novel self-supervised tasks using video data, the learned features are merely
on a frame-by-frame basis, which are not applicable to many video analytic
tasks where spatio-temporal features are prevailing. In this paper we propose a
novel self-supervised approach to learn spatio-temporal features for video
representation. Inspired by the success of two-stream approaches in video
classification, we propose to learn visual features by regressing both motion
and appearance statistics along spatial and temporal dimensions, given only the
input video data. Specifically, we extract statistical concepts (fast-motion
region and the corresponding dominant direction, spatio-temporal color
diversity, dominant color, etc.) from simple patterns in both spatial and
temporal domains. Unlike prior puzzles that are even hard for humans to solve,
the proposed approach is consistent with human inherent visual habits and
therefore easy to answer. We conduct extensive experiments with C3D to validate
the effectiveness of our proposed approach. The experiments show that our
approach can significantly improve the performance of C3D when applied to video
classification tasks. Code is available at
https://github.com/laura-wang/video_repres_mas. | [
"cs.CV"
] |
This paper presents a novel unsupervised segmentation method for 3D medical
images. Convolutional neural networks (CNNs) have brought significant advances
in image segmentation. However, most of the recent methods rely on supervised
learning, which requires large amounts of manually annotated data. Thus, it is
challenging for these methods to cope with the growing amount of medical
images. This paper proposes a unified approach to unsupervised deep
representation learning and clustering for segmentation. Our proposed method
consists of two phases. In the first phase, we learn deep feature
representations of training patches from a target image using joint
unsupervised learning (JULE) that alternately clusters representations
generated by a CNN and updates the CNN parameters using cluster labels as
supervisory signals. We extend JULE to 3D medical images by utilizing 3D
convolutions throughout the CNN architecture. In the second phase, we apply
k-means to the deep representations from the trained CNN and then project
cluster labels to the target image in order to obtain the fully segmented
image. We evaluated our methods on three images of lung cancer specimens
scanned with micro-computed tomography (micro-CT). The automatic segmentation
of pathological regions in micro-CT could further contribute to the
pathological examination process. Hence, we aim to automatically divide each
image into the regions of invasive carcinoma, noninvasive carcinoma, and normal
tissue. Our experiments show the potential abilities of unsupervised deep
representation learning for medical image segmentation. | [
"cs.CV"
] |
We present an approach for adapting convolutional neural networks for object
recognition and classification to scientific literature layout detection
(SLLD), a shared subtask of several information extraction problems. Scientific
publications contain multiple types of information sought by researchers in
various disciplines, organized into an abstract, bibliography, and sections
documenting related work, experimental methods, and results; however, there is
no effective way to extract this information due to their diverse layout. In
this paper, we present a novel approach to developing an end-to-end learning
framework to segment and classify major regions of a scientific document. We
consider scientific document layout analysis as an object detection task over
digital images, without any additional text features that need to be added into
the network during the training process. Our technical objective is to
implement transfer learning via fine-tuning of pre-trained networks and thereby
demonstrate that this deep learning architecture is suitable for tasks that
lack very large document corpora for training ab initio. As part of the
experimental test bed for empirical evaluation of this approach, we created a
merged multi-corpus data set for scientific publication layout detection tasks.
Our results show good improvement with fine-tuning of a pre-trained base
network using this merged data set, compared to the baseline convolutional
neural network architecture. | [
"cs.CV",
"cs.LG",
"I.2.6; I.2.10; I.4.9; I.5.1; I.5.4"
] |
Social media images are generally transformed by filtering to obtain
aesthetically more pleasing appearances. However, CNNs generally fail to
interpret both the image and its filtered version as the same in the visual
analysis of social media images. We introduce Instagram Filter Removal Network
(IFRNet) to mitigate the effects of image filters for social media analysis
applications. To achieve this, we assume any filter applied to an image
substantially injects a piece of additional style information to it, and we
consider this problem as a reverse style transfer problem. The visual effects
of filtering can be directly removed by adaptively normalizing external style
information in each level of the encoder. Experiments demonstrate that IFRNet
outperforms all compared methods in quantitative and qualitative comparisons,
and has the ability to remove the visual effects to a great extent.
Additionally, we present the filter classification performance of our proposed
model, and analyze the dominant color estimation on the images unfiltered by
all compared methods. | [
"cs.CV"
] |
Classification and regression are two pillars of object detectors. In most
CNN-based detectors, these two pillars are optimized independently. Without
direct interactions between them, the classification loss and the regression
loss can not be optimized synchronously toward the optimal direction in the
training phase. This clearly leads to lots of inconsistent predictions with
high classification score but low localization accuracy or low classification
score but high localization accuracy in the inference phase, especially for the
objects of irregular shape and occlusion, which severely hurts the detection
performance of existing detectors after NMS. To reconcile prediction
consistency for balanced object detection, we propose a Harmonic loss to
harmonize the optimization of classification branch and localization branch.
The Harmonic loss enables these two branches to supervise and promote each
other during training, thereby producing consistent predictions with high
co-occurrence of top classification and localization in the inference phase.
Furthermore, in order to prevent the localization loss from being dominated by
outliers during training phase, a Harmonic IoU loss is proposed to harmonize
the weight of the localization loss of different IoU-level samples.
Comprehensive experiments on benchmarks PASCAL VOC and MS COCO demonstrate the
generality and effectiveness of our model for facilitating existing object
detectors to state-of-the-art accuracy. | [
"cs.CV"
] |
Although graph neural networks (GNNs) have made great progress recently on
learning from graph-structured data in practice, their theoretical guarantee on
generalizability remains elusive in the literature. In this paper, we provide a
theoretically-grounded generalizability analysis of GNNs with one hidden layer
for both regression and binary classification problems. Under the assumption
that there exists a ground-truth GNN model (with zero generalization error),
the objective of GNN learning is to estimate the ground-truth GNN parameters
from the training data. To achieve this objective, we propose a learning
algorithm that is built on tensor initialization and accelerated gradient
descent. We then show that the proposed learning algorithm converges to the
ground-truth GNN model for the regression problem, and to a model sufficiently
close to the ground-truth for the binary classification problem. Moreover, for
both cases, the convergence rate of the proposed learning algorithm is proven
to be linear and faster than the vanilla gradient descent algorithm. We further
explore the relationship between the sample complexity of GNNs and their
underlying graph properties. Lastly, we provide numerical experiments to
demonstrate the validity of our analysis and the effectiveness of the proposed
learning algorithm for GNNs. | [
"cs.LG",
"eess.SP",
"math.OC",
"stat.ML"
] |
We consider the problem of discriminatively learning restricted Boltzmann
machines in the presence of relational data. Unlike previous approaches that
employ a rule learner (for structure learning) and a weight learner (for
parameter learning) sequentially, we develop a gradient-boosted approach that
performs both simultaneously. Our approach learns a set of weak relational
regression trees, whose paths from root to leaf are conjunctive clauses and
represent the structure, and whose leaf values represent the parameters. When
the learned relational regression trees are transformed into a lifted RBM, its
hidden nodes are precisely the conjunctive clauses derived from the relational
regression trees. This leads to a more interpretable and explainable model. Our
empirical evaluations clearly demonstrate this aspect, while displaying no loss
in effectiveness of the learned models. | [
"cs.LG",
"cs.AI"
] |
Precise boundary annotations of image regions can be crucial for downstream
applications which rely on region-class semantics. Some document collections
contain densely laid out, highly irregular and overlapping multi-class region
instances with large range in aspect ratio. Fully automatic boundary estimation
approaches tend to be data intensive, cannot handle variable-sized images and
produce sub-optimal results for aforementioned images. To address these issues,
we propose BoundaryNet, a novel resizing-free approach for high-precision
semi-automatic layout annotation. The variable-sized user selected region of
interest is first processed by an attention-guided skip network. The network
optimization is guided via Fast Marching distance maps to obtain a good quality
initial boundary estimate and an associated feature representation. These
outputs are processed by a Residual Graph Convolution Network optimized using
Hausdorff loss to obtain the final region boundary. Results on a challenging
image manuscript dataset demonstrate that BoundaryNet outperforms strong
baselines and produces high-quality semantic region boundaries. Qualitatively,
our approach generalizes across multiple document image datasets containing
different script systems and layouts, all without additional fine-tuning. We
integrate BoundaryNet into a document annotation system and show that it
provides high annotation throughput compared to manual and fully automatic
alternatives. | [
"cs.CV",
"cs.CL",
"cs.MM"
] |
We consider the problem of enriching current object detection systems with
veridical object sizes and relative depth estimates from a single image. There
are several technical challenges to this, such as occlusions, lack of
calibration data and the scale ambiguity between object size and distance.
These have not been addressed in full generality in previous work. Here we
propose to tackle these issues by building upon advances in object recognition
and using recently created large-scale datasets. We first introduce the task of
amodal bounding box completion, which aims to infer the the full extent of the
object instances in the image. We then propose a probabilistic framework for
learning category-specific object size distributions from available annotations
and leverage these in conjunction with amodal completion to infer veridical
sizes in novel images. Finally, we introduce a focal length prediction approach
that exploits scene recognition to overcome inherent scaling ambiguities and we
demonstrate qualitative results on challenging real-world scenes. | [
"cs.CV"
] |
Hyperspectral imaging allows for analysis of images in several hundred of
spectral bands depending on the spectral resolution of the imaging sensor.
Hyperspectral document image is the one which has been captured by a
hyperspectral camera so that the document can be observed in the different
bands on the basis of their unique spectral signatures. To detect the forgery
in a document various Ink mismatch detection techniques based on hyperspectral
imaging have presented vast potential in differentiating visually similar inks.
Inks of different materials exhibit different spectral signature even if they
have the same color. Hyperspectral analysis of document images allows
identification and discrimination of visually similar inks. Based on this
analysis forensic experts can identify the authenticity of the document. In
this paper an extensive ink mismatch detection technique is presented which
uses KMean Clustering to identify different inks on the basis of their unique
spectral response and separates them into different clusters. | [
"cs.CV",
"cs.CR"
] |
People live in a 3D world. However, existing works on person
re-identification (re-id) mostly consider the semantic representation learning
in a 2D space, intrinsically limiting the understanding of people. In this
work, we address this limitation by exploring the prior knowledge of the 3D
body structure. Specifically, we project 2D images to a 3D space and introduce
a novel parameter-efficient Omni-scale Graph Network (OG-Net) to learn the
pedestrian representation directly from 3D point clouds. OG-Net effectively
exploits the local information provided by sparse 3D points and takes advantage
of the structure and appearance information in a coherent manner. With the help
of 3D geometry information, we can learn a new type of deep re-id feature free
from noisy variants, such as scale and viewpoint. To our knowledge, we are
among the first attempts to conduct person re-identification in the 3D space.
We demonstrate through extensive experiments that the proposed method (1) eases
the matching difficulty in the traditional 2D space, (2) exploits the
complementary information of 2D appearance and 3D structure, (3) achieves
competitive results with limited parameters on four large-scale person re-id
datasets, and (4) has good scalability to unseen datasets. Our code, models and
generated 3D human data are publicly available at
https://github.com/layumi/person-reid-3d . | [
"cs.CV"
] |
Adversarial attacks on machine learning-based classifiers, along with defense
mechanisms, have been widely studied in the context of single-label
classification problems. In this paper, we shift the attention to multi-label
classification, where the availability of domain knowledge on the relationships
among the considered classes may offer a natural way to spot incoherent
predictions, i.e., predictions associated to adversarial examples lying outside
of the training data distribution. We explore this intuition in a framework in
which first-order logic knowledge is converted into constraints and injected
into a semi-supervised learning problem. Within this setting, the constrained
classifier learns to fulfill the domain knowledge over the marginal
distribution, and can naturally reject samples with incoherent predictions.
Even though our method does not exploit any knowledge of attacks during
training, our experimental analysis surprisingly unveils that domain-knowledge
constraints can help detect adversarial examples effectively, especially if
such constraints are not known to the attacker. | [
"cs.LG",
"cs.CR",
"stat.ML"
] |
With the fast development of quantum computing and deep learning, quantum
neural networks have attracted great attention recently. By leveraging the
power of quantum computing, deep neural networks can potentially overcome
computational power limitations in classic machine learning. However, when
multiple quantum machines wish to train a global model using the local data on
each machine, it may be very difficult to copy the data into one machine and
train the model. Therefore, a collaborative quantum neural network framework is
necessary. In this article, we borrow the core idea of federated learning to
propose QuantumFed, a quantum federated learning framework to have multiple
quantum nodes with local quantum data train a mode together. Our experiments
show the feasibility and robustness of our framework. | [
"cs.LG",
"cs.DC"
] |
We present a method for metric optimization in the Large Deformation
Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced
Riemannian metric on the space of diffeomorphisms as a kernel in a machine
learning context. For simplicity, we choose the kernel Fischer Linear
Discriminant Analysis (KLDA) as the framework. Optimizing the kernel parameters
in an Expectation-Maximization framework, we define model fidelity via the
hinge loss of the decision function. The resulting algorithm optimizes the
parameters of the LDDMM norm-inducing differential operator as a solution to a
group-wise registration and classification problem. In practice, this may lead
to a biology-aware registration, focusing its attention on the predictive task
at hand such as identifying the effects of disease. We first tested our
algorithm on a synthetic dataset, showing that our parameter selection improves
registration quality and classification accuracy. We then tested the algorithm
on 3D subcortical shapes from the Schizophrenia cohort Schizconnect. Our
Schizpohrenia-Control predictive model showed significant improvement in ROC
AUC compared to baseline parameters. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
Deep learning's performance has been extensively recognized recently. Graph
neural networks (GNNs) are designed to deal with graph-structural data that
classical deep learning does not easily manage. Since most GNNs were created
using distinct theories, direct comparisons are impossible. Prior research has
primarily concentrated on categorizing existing models, with little attention
paid to their intrinsic connections. The purpose of this study is to establish
a unified framework that integrates GNNs based on spectral graph and
approximation theory. The framework incorporates a strong integration between
spatial- and spectral-based GNNs while tightly associating approaches that
exist within each respective domain. | [
"cs.LG",
"cs.AI"
] |
Many machine learning image classifiers are vulnerable to adversarial
attacks, inputs with perturbations designed to intentionally trigger
misclassification. Current adversarial methods directly alter pixel colors and
evaluate against pixel norm-balls: pixel perturbations smaller than a specified
magnitude, according to a measurement norm. This evaluation, however, has
limited practical utility since perturbations in the pixel space do not
correspond to underlying real-world phenomena of image formation that lead to
them and has no security motivation attached. Pixels in natural images are
measurements of light that has interacted with the geometry of a physical
scene. As such, we propose the direct perturbation of physical parameters that
underly image formation: lighting and geometry. As such, we propose a novel
evaluation measure, parametric norm-balls, by directly perturbing physical
parameters that underly image formation. One enabling contribution we present
is a physically-based differentiable renderer that allows us to propagate pixel
gradients to the parametric space of lighting and geometry. Our approach
enables physically-based adversarial attacks, and our differentiable renderer
leverages models from the interactive rendering literature to balance the
performance and accuracy trade-offs necessary for a memory-efficient and
scalable adversarial data augmentation workflow. | [
"cs.LG",
"cs.CV",
"cs.GR",
"stat.ML"
] |
The ability to synthesize style and content of different images to form a
visually coherent image holds great promise in various applications such as
stylistic painting, design prototyping, image editing, and augmented reality.
However, the majority of works in image style transfer have focused on
transferring the style of an image to the entirety of another image, and only a
very small number of works have experimented on methods to transfer style to an
instance of another image. Researchers have proposed methods to circumvent the
difficulty of transferring style to an instance in an arbitrary shape. In this
paper, we propose a topologically inspired algorithm called Forward Stretching
to tackle this problem by transforming an instance into a tensor
representation, which allows us to transfer style to this instance itself
directly. Forward Stretching maps pixels to specific positions and interpolate
values between pixels to transform an instance to a tensor. This algorithm
allows us to introduce a method to transfer arbitrary style to an instance in
an arbitrary shape. We showcase the results of our method in this paper. | [
"cs.CV"
] |
Deep learning models exhibit a preference for statistical fitting over
logical reasoning. Spurious correlations might be memorized when there exists
statistical bias in training data, which severely limits the model performance
especially in small data scenarios. In this work, we introduce Counterfactual
Adversarial Training framework (CAT) to tackle the problem from a causality
perspective. Particularly, for a specific sample, CAT first generates a
counterfactual representation through latent space interpolation in an
adversarial manner, and then performs Counterfactual Risk Minimization (CRM) on
each original-counterfactual pair to adjust sample-wise loss weight
dynamically, which encourages the model to explore the true causal effect.
Extensive experiments demonstrate that CAT achieves substantial performance
improvement over SOTA across different downstream tasks, including sentence
classification, natural language inference and question answering. | [
"cs.LG"
] |
This study investigates the theoretical foundations of t-distributed
stochastic neighbor embedding (t-SNE), a popular nonlinear dimension reduction
and data visualization method. A novel theoretical framework for the analysis
of t-SNE based on the gradient descent approach is presented. For the early
exaggeration stage of t-SNE, we show its asymptotic equivalence to a power
iteration based on the underlying graph Laplacian, characterize its limiting
behavior, and uncover its deep connection to Laplacian spectral clustering, and
fundamental principles including early stopping as implicit regularization. The
results explain the intrinsic mechanism and the empirical benefits of such a
computational strategy. For the embedding stage of t-SNE, we characterize the
kinematics of the low-dimensional map throughout the iterations, and identify
an amplification phase, featuring the intercluster repulsion and the expansive
behavior of the low-dimensional map. The general theory explains the fast
convergence rate and the exceptional empirical performance of t-SNE for
visualizing clustered data, brings forth the interpretations of the t-SNE
output, and provides theoretical guidance for selecting tuning parameters in
various applications. | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.TH"
] |
Multi-view representation learning captures comprehensive information from
multiple views of a shared context. Recent works intuitively apply contrastive
learning (CL) to learn representations, regarded as a pairwise manner, which is
still scalable: view-specific noise is not filtered in learning view-shared
representations; the fake negative pairs, where the negative terms are actually
within the same class as the positive, and the real negative pairs are
coequally treated; and evenly measuring the similarities between terms might
interfere with optimization. Importantly, few works research the theoretical
framework of generalized self-supervised multi-view learning, especially for
more than two views. To this end, we rethink the existing multi-view learning
paradigm from the information theoretical perspective and then propose a novel
information theoretical framework for generalized multi-view learning. Guided
by it, we build a multi-view coding method with a three-tier progressive
architecture, namely Information theory-guided heuristic Progressive Multi-view
Coding (IPMC). In the distribution-tier, IPMC aligns the distribution between
views to reduce view-specific noise. In the set-tier, IPMC builds self-adjusted
pools for contrasting, which utilizes a view filter to adaptively modify the
pools. Lastly, in the instance-tier, we adopt a designed unified loss to learn
discriminative representations and reduce the gradient interference.
Theoretically and empirically, we demonstrate the superiority of IPMC over
state-of-the-art methods. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
The analysis of nonconvex matrix completion has recently attracted much
attention in the community of machine learning thanks to its computational
convenience. Existing analysis on this problem, however, usually relies on
$\ell_{2,\infty}$ projection or regularization that involves unknown model
parameters, although they are observed to be unnecessary in numerical
simulations, see, e.g., Zheng and Lafferty [2016]. In this paper, we extend the
analysis of the vanilla gradient descent for positive semidefinite matrix
completion proposed in Ma et al. [2017] to the rectangular case, and more
significantly, improve the required sampling rate from
$O(\operatorname{poly}(\kappa)\mu^3 r^3 \log^3 n/n )$ to $O(\mu^2 r^2
\kappa^{14} \log n/n )$. Our technical ideas and contributions are potentially
useful in improving the leave-one-out analysis in other related problems. | [
"stat.ML",
"cs.LG"
] |
Change Captioning is a task that aims to describe the difference between
images with natural language. Most existing methods treat this problem as a
difference judgment without the existence of distractors, such as viewpoint
changes. However, in practice, viewpoint changes happen often and can overwhelm
the semantic difference to be described. In this paper, we propose a novel
visual encoder to explicitly distinguish viewpoint changes from semantic
changes in the change captioning task. Moreover, we further simulate the
attention preference of humans and propose a novel reinforcement learning
process to fine-tune the attention directly with language evaluation rewards.
Extensive experimental results show that our method outperforms the
state-of-the-art approaches by a large margin in both Spot-the-Diff and
CLEVR-Change datasets. | [
"cs.CV"
] |
User behaviour targeting is essential in online advertising. Compared with
sponsored search keyword targeting and contextual advertising page content
targeting, user behaviour targeting builds users' interest profiles via
tracking their online behaviour and then delivers the relevant ads according to
each user's interest, which leads to higher targeting accuracy and thus more
improved advertising performance. The current user profiling methods include
building keywords and topic tags or mapping users onto a hierarchical taxonomy.
However, to our knowledge, there is no previous work that explicitly
investigates the user online visits similarity and incorporates such similarity
into their ad response prediction. In this work, we propose a general framework
which learns the user profiles based on their online browsing behaviour, and
transfers the learned knowledge onto prediction of their ad response.
Technically, we propose a transfer learning model based on the probabilistic
latent factor graphic models, where the users' ad response profiles are
generated from their online browsing profiles. The large-scale experiments
based on real-world data demonstrate significant improvement of our solution
over some strong baselines. | [
"cs.LG",
"cs.IR"
] |
The generation of plausible and controllable 3D human motion animations is a
long-standing problem that often requires a manual intervention of skilled
artists. Existing machine learning approaches try to semi-automate this process
by allowing the user to input partial information about the future movement.
However, they are limited in two significant ways: they either base their pose
prediction on past prior frames with no additional control over the future
poses or allow the user to input only a single trajectory that precludes
fine-grained control over the output. To mitigate these two issues, we
reformulate the problem of future pose prediction into pose completion in space
and time where trajectories are represented as poses with missing joints. We
show that such a framework can generalize to other neural networks designed for
future pose prediction. Once trained in this framework, a model is capable of
predicting sequences from any number of trajectories. To leverage this notion,
we propose a novel transformer-like architecture, TrajeVAE, that provides a
versatile framework for 3D human animation. We demonstrate that TrajeVAE
outperforms trajectory-based reference approaches and methods that base their
predictions on past poses in terms of accuracy. We also show that it can
predict reasonable future poses even if provided only with an initial pose. | [
"cs.CV",
"cs.AI"
] |
Several works based on Generative Adversarial Networks (GAN) have been
recently proposed to predict a set of medical images from a single modality
(e.g, FLAIR MRI from T1 MRI). However, such frameworks are primarily designed
to operate on images, limiting their generalizability to non-Euclidean
geometric data such as brain graphs. While a growing number of connectomic
studies has demonstrated the promise of including brain graphs for diagnosing
neurological disorders, no geometric deep learning work was designed for
multiple target brain graphs prediction from a source brain graph. Despite the
momentum the field of graph generation has gained in the last two years,
existing works have two critical drawbacks. First, the bulk of such works aims
to learn one model for each target domain to generate from a source domain.
Thus, they have a limited scalability in jointly predicting multiple target
domains. Second, they merely consider the global topological scale of a graph
(i.e., graph connectivity structure) and overlook the local topology at the
node scale of a graph (e.g., how central a node is in the graph). To meet these
challenges, we introduce MultiGraphGAN architecture, which not only predicts
multiple brain graphs from a single brain graph but also preserves the
topological structure of each target graph to predict. Its three core
contributions lie in: (i) designing a graph adversarial auto-encoder for
jointly predicting brain graphs from a single one, (ii) handling the mode
collapse problem of GAN by clustering the encoded source graphs and proposing a
cluster-specific decoder, (iii) introducing a topological loss to force the
reconstruction of topologically sound target brain graphs. Our MultiGraphGAN
significantly outperformed its variants thereby showing its great potential in
multi-view brain graph generation from a single graph. | [
"cs.LG",
"stat.ML"
] |
Deep learning has transformed computer vision, natural language processing,
and speech recognition\cite{badrinarayanan2017segnet, dong2016image,
ren2017faster, ji20133d}. However, two critical questions remain obscure: (1)
why do deep neural networks generalize better than shallow networks; and (2)
does it always hold that a deeper network leads to better performance?
Specifically, letting $L$ be the number of convolutional and pooling layers in
a deep neural network, and $n$ be the size of the training sample, we derive an
upper bound on the expected generalization error for this network, i.e.,
\begin{eqnarray*}
\mathbb{E}[R(W)-R_S(W)] \leq
\exp{\left(-\frac{L}{2}\log{\frac{1}{\eta}}\right)}\sqrt{\frac{2\sigma^2}{n}I(S,W)
}
\end{eqnarray*} where $\sigma >0$ is a constant depending on the loss
function, $0<\eta<1$ is a constant depending on the information loss for each
convolutional or pooling layer, and $I(S, W)$ is the mutual information between
the training sample $S$ and the output hypothesis $W$. This upper bound shows
that as the number of convolutional and pooling layers $L$ increases in the
network, the expected generalization error will decrease exponentially to zero.
Layers with strict information loss, such as the convolutional layers, reduce
the generalization error for the whole network; this answers the first
question. However, algorithms with zero expected generalization error does not
imply a small test error or $\mathbb{E}[R(W)]$. This is because
$\mathbb{E}[R_S(W)]$ is large when the information for fitting the data is lost
as the number of layers increases. This suggests that the claim `the deeper the
better' is conditioned on a small training error or $\mathbb{E}[R_S(W)]$.
Finally, we show that deep learning satisfies a weak notion of stability and
the sample complexity of deep neural networks will decrease as $L$ increases. | [
"stat.ML",
"cs.LG"
] |
A large number of studies analyse object detection and pose estimation at
visual level in 2D, discussing the effects of challenges such as occlusion,
clutter, texture, etc., on the performances of the methods, which work in the
context of RGB modality. Interpreting the depth data, the study in this paper
presents thorough multi-modal analyses. It discusses the above-mentioned
challenges for full 6D object pose estimation in RGB-D images comparing the
performances of several 6D detectors in order to answer the following
questions: What is the current position of the computer vision community for
maintaining "automation" in robotic manipulation? What next steps should the
community take for improving "autonomy" in robotics while handling objects? Our
findings include: (i) reasonably accurate results are obtained on
textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy
existence of occlusion and clutter severely affects the detectors, and
similar-looking distractors is the biggest challenge in recovering instances'
6D. (iii) Template-based methods and random forest-based learning algorithms
underlie object detection and 6D pose estimation. Recent paradigm is to learn
deep discriminative feature representations and to adopt CNNs taking RGB images
as input. (iv) Depending on the availability of large-scale 6D annotated depth
datasets, feature representations can be learnt on these datasets, and then the
learnt representations can be customized for the 6D problem. | [
"cs.CV"
] |
Supervised classification and representation learning are two widely used
classes of methods to analyze multivariate images. Although complementary,
these methods have been scarcely considered jointly in a hierarchical modeling.
In this paper, a method coupling these two approaches is designed using a
matrix cofactorization formulation. Each task is modeled as a factorization
matrix problem and a term relating both coding matrices is then introduced to
drive an appropriate coupling. The link can be interpreted as a clustering
operation over a low-dimensional representation vectors. The attribution
vectors of the clustering are then used as features vectors for the
classification task, i.e., the coding vectors of the corresponding
factorization problem. A proximal gradient descent algorithm, ensuring
convergence to a critical point of the objective function, is then derived to
solve the resulting non-convex non-smooth optimization problem. An evaluation
of the proposed method is finally conducted both on synthetic and real data in
the specific context of hyperspectral image interpretation, unifying two
standard analysis techniques, namely unmixing and classification. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
This paper aims to establish a framework for extreme learning machines (ELMs)
on general hypercomplex algebras. Hypercomplex neural networks are machine
learning models that feature higher-dimension numbers as parameters, inputs,
and outputs. Firstly, we review broad hypercomplex algebras and show a
framework to operate in these algebras through real-valued linear algebra
operations in a robust manner. We proceed to explore a handful of well-known
four-dimensional examples. Then, we propose the hypercomplex-valued ELMs and
derive their learning using a hypercomplex-valued least-squares problem.
Finally, we compare real and hypercomplex-valued ELM models' performance in an
experiment on time-series prediction and another on color image auto-encoding.
The computational experiments highlight the excellent performance of
hypercomplex-valued ELMs to treat high-dimensional data, including models based
on unusual hypercomplex algebras. | [
"cs.LG"
] |
Camera traps are used worldwide to monitor wildlife. Despite the increasing
availability of Deep Learning (DL) models, the effective usage of this
technology to support wildlife monitoring is limited. This is mainly due to the
complexity of DL technology and high computing requirements. This paper
presents the implementation of the light-weight and state-of-the-art YOLOv5
architecture for automated labeling of camera trap images of mammals in the
Bialowieza Forest (BF), Poland. The camera trapping data were organized and
harmonized using TRAPPER software, an open source application for managing
large-scale wildlife monitoring projects. The proposed image recognition
pipeline achieved an average accuracy of 85% F1-score in the identification of
the 12 most commonly occurring medium-size and large mammal species in BF using
a limited set of training and testing data (a total 2659 images with animals).
Based on the preliminary results, we concluded that the YOLOv5 object
detection and classification model is a promising light-weight DL solution
after the adoption of transfer learning technique. It can be efficiently
plugged in via an API into existing web-based camera trapping data processing
platforms such as e.g. TRAPPER system. Since TRAPPER is already used to manage
and classify (manually) camera trapping datasets by many research groups in
Europe, the implementation of AI-based automated species classification may
significantly speed up the data processing workflow and thus better support
data-driven wildlife monitoring and conservation. Moreover, YOLOv5 developers
perform better performance on edge devices which may open a new chapter in
animal population monitoring in real time directly from camera trap devices. | [
"cs.CV",
"cs.LG",
"eess.IV",
"68T07",
"I.2; I.4"
] |
Learning compact binary codes for image retrieval task using deep neural
networks has attracted increasing attention recently. However, training deep
hashing networks for the task is challenging due to the binary constraints on
the hash codes, the similarity preserving property, and the requirement for a
vast amount of labelled images. To the best of our knowledge, none of the
existing methods has tackled all of these challenges completely in a unified
framework. In this work, we propose a novel end-to-end deep learning approach
for the task, in which the network is trained to produce binary codes directly
from image pixels without the need of manual annotation. In particular, to deal
with the non-smoothness of binary constraints, we propose a novel pairwise
constrained loss function, which simultaneously encodes the distances between
pairs of hash codes, and the binary quantization error. In order to train the
network with the proposed loss function, we propose an efficient parameter
learning algorithm. In addition, to provide similar / dissimilar training
images to train the network, we exploit 3D models reconstructed from unlabelled
images for automatic generation of enormous training image pairs. The extensive
experiments on image retrieval benchmark datasets demonstrate the improvements
of the proposed method over the state-of-the-art compact representation methods
on the image retrieval problem. | [
"cs.CV"
] |
The resemblance between the methods used in quantum-many body physics and in
machine learning has drawn considerable attention. In particular, tensor
networks (TNs) and deep learning architectures bear striking similarities to
the extent that TNs can be used for machine learning. Previous results used
one-dimensional TNs in image recognition, showing limited scalability and
flexibilities. In this work, we train two-dimensional hierarchical TNs to solve
image recognition problems, using a training algorithm derived from the
multi-scale entanglement renormalization ansatz. This approach introduces
mathematical connections among quantum many-body physics, quantum information
theory, and machine learning. While keeping the TN unitary in the training
phase, TN states are defined, which encode classes of images into quantum
many-body states. We study the quantum features of the TN states, including
quantum entanglement and fidelity. We find these quantities could be properties
that characterize the image classes, as well as the machine learning tasks. | [
"stat.ML",
"cond-mat.str-el",
"physics.comp-ph",
"quant-ph"
] |
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